%%%
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%%% ----------------------------------------
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%%% Source : http://irabot.inrialpes.fr/skel?projet=modemic
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%%% Ce document est un squelette de rapport d'activit\'e
%%% mis \`a jour \`a partir des bases de donn\'ees de l'INRIA
%%% (HAL pour les publi, BASTRI pour les \'equipes, BIL pour les logiciels ...).
%%% Le r\'edacteur peut compl\'eter ce document et adapter la bibliographie.
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%%% R\'ecuperer l'archive tgz de 2011 et la d\'ecompresser
%%% dans un r\'epertoire. Il y a un fichier tex et 3 fichiers de bibliographie (.bib) :
%%% - modemic2011.tex : le texte en latex du rapport
%%% - modemic2011.bib : les publis de l'ann\'ee 2011 issues de Hal
%%% - modemic_refer2011.bub : les publications de r\'ef\'erence (c'est-a-dire les publis les plus importantes
%%%   de l'\'equipe quelle que soit l'ann\'ee)
%%% - modemic_foot2011.bib : les publis plac\'ees dans les notes de bas de page
%%%  (footnote) issus de votre rapport 2010
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%%% Instructions pour l'\'ecriture du RA :
%%% http://www.inria.fr/interne/disc/publier/raweb.html
%%%
%%% Pour compiler le rapport utiliser le serveur iRAbot :
%%% http://irabot.inrialpes.fr/irabot
%%% ou bien le script irabot.sh qui utilise iRAbot.
%%% Certains centre ont leur propre serveur de compilation.
%%%
%%% Le rapport est en ANGLAIS
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\documentclass{ra2011}

%SKEL - v0.4 - 2011 (Utiliser pour les stats d'utilisation de Skel, merci de ne effacer cette ligne)
% ne pas enlever
\renewenvironment{motscle}{\begin{xmlelement}{keywords}}{\end{xmlelement}}

%%% Par defaut sont inclus les packages : html, french, graphics et footbib
%%% (ifthen curves soul epsf html)
%%% Le plus souvent la commande \usepackage n'est pas pris en compte
%%% Mais si le package est calc ou fp certaines commandes sont rendues disponibles
%%% (fancyvrb)

%%% Mettez ici les \newcommand et \def que vous voulez


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%%%
%%%  Information sur les donn\'ees import\'ees : Bastri
%%%
%%% Les organismes ou \'ecoles partenaires de votre \'equipe ainsi que les labos auxquels
%%% vous \^etes associ\'es seront affich\'es automatiquement et \`a post\'eriori, comme vos CRI,
%%% th\`eme et domaine de rattachement. Ces infos sont issues de Bastri :
%%% https://bastri.inria.fr, la base des structures de recherche INRIA.
%%%
%%% L'acronyme de votre \'equipe sera \'egalement pr\'esent\'e comme dans les fiches projets.
%%% Si vous souhaitez le modifier, faites-le dans la base de gestion des fiches projets :
%%% https://bastri.inria.fr/FichesProjets/ et r\'eg\'en\'erez votre trame.
%%%
%%% Le "moreinfo" de l'\'equipe servira donc uniquement \`a pr\'eciser la localisation
%%% quand elle est distincte du CRI (pour ceux qui le souhaitent).
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%%% V\'erifiez et signalez erreurs et probl\`emes : raweb-support@inria.fr
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%%% \projet{<PROJET>}{<ALT-ABREGE>}{<NOM-PROJET-EXPLICITE>}
%%% exemple :
%%% \projet{EXEMPLE}{ExemplE}{Algebraic Systems for Research and Industry}
%%%


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% Informations concernant l'equipe extraites de BASTRI
% Source : http://bastri.inria.fr/
% Date : mardi 15 novembre 2011, 10:04:17 (UTC+0100)
%

\projet{MODEMIC}{modemic}{Modelling and Optimisation of the
Dynamics of Ecosystems with MICro-organisms}

%% Pour information le domaine et le theme du projet
%% Domaine : Computational Sciences for Biology, Medicine and the Environment
%% Theme : Observation, Modeling, and Control for Life Sciences


%%% Type de groupe
%%% EPI - \'equipe - action exploratoire
%%% Utilisez oui si EPI, non si \'equipe ou bien AE pour action exploratoire
%%% oui - non - AE

\isproject{non}


%%% CRI INRIA
%%% Sophia, ou Rocquencourt, ou Nancy, etc

\UR{Sophia}


%%% Des mots-cl\'es d\'ecrivant l'activit\'e de votre \'equipe ont \'et\'e pr\'e-remplis.
%%% Il est possible de les modifier ou de les remplacer mais utiliser pour cela de pr\'ef\'erence
%%% ceux indiqu\'es dans la liste compl\`ete disponible \`a l'adresse http://irabot.inria.fr/keywords
%%% S'il vous manque un terme important, vous pouvez n\'eanmoins ajouter votre propre mot-cl\'e.
%%% Le but est de fournir au total autour de 5 mots-cl\'es, 6 au maximum.
%%% Le s\'eparateur entre les mots est le ; (point-virgule).


\begin{motscle}
%Modelling, microbial ecology, environment preservation, biodiversity, dynamical systems, optimisation, individual-based %models, spatio-temporal dynamics, multi-scale, numerical simulations.
Models;
Microbial Ecology;
Control Theory;
Population Modeling;
Multiscale Models;
Individual-based Models.
\end{motscle}


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\begin{document}
\maketitle
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%%% Attention, il n'y a plus de \nocite{*} par defaut.
%%% Vous pouvez mettre des \nocite{xx}, ou meme un \nocite{*}

%\nocite{TFE98}
%\nocite{AGATHISC}
%\nocite{FD98}
%\nocite{x,y,z}

%%% Pour ceux qui le souhaitent le more info permet d'inserer un texte
%%% de 3 - 4 lignes qui indique les particularit\'es de l'\'equipe
%%% Il permet par exemple de pr\'eciser la localisation quand elle est distincte du CRI.
%%% Ne doublonnez pas th\`eme, domaine, CRI et partenariats qui sont ajout\'es automatiquement.


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\begin{moreinfo}
MODEMIC is a common INRA-INRIA team that aims at sharing skills of researchers of both Institutes for developing, analysing and simulating new models of microbial ecosystems as efficient tools to understand, explore, pilot and manage industrial and
natural bioprocesses.

\end{moreinfo}
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%%%
%%% Liste des sections possibles pour les modules :
%%% 		presentation* 
%%% 		fondements 
%%% 		domaine 
%%% 		logiciels
%%% 		resultats 
%%% 		contrats* 
%%% 		international* 
%%% 		diffusion*
%%% ou bien c'est vide
%%%
%%% ex : \begin{module}{composition}
%%%
%%% \begin{module} {<SECTION>} {<NOMMODULE>} {<TITRE>}
%%%     <PERSONNES>
%%%    [<GLOSSAIRE>]
%%%    [<MOREINFO>]
%%%    [<RESUME>]
%%%    <CORPS>
%%% \end{module}
%%%
%%% si SECTION est vide on utilise celui du precedent module
%%%
%%% NOMMODULE est un identifiant unique pour reperer le module,
%%% aussi chaque module doit en avoir un NOMMODULE different
%%%
%%% \begin{module}{}{calcul-formel}{Sofrware aspects of Computer Algebra}
%%%  \begin{participants}
%%%  format: \pers <PRENOM> [<PARTICULE>] <NOM> [<MOREINFO>]
%%%        \pers{Jean}[de]{La Fontaine}[1621-1695],
%%%        \pers{Cecil Blount}{De Mille}
%%%  \end{participants}
%%%  \begin{glossaire}
%%%       \glo{backward combatability}{A property of hardware or software
%%%   ... but activeliy... }
%%%  \end{glossaire}
%%%  \begin{abstract}
%%%       Le joli r\'esum\'e que voil\`a
%%%  \end{abstact}
%%%  This is a very short module with a hypertext link to the
%%%  \href{http://www.eps.mcgill.ca/jargon/jargon.html}{Jargon File}.
%%% \end{module}
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%%%
%%% Composition de l'equipe
%%%
%%% Professions possibles :
%%% Visiteur Chercheur Enseignant Technique Assistant
%%% PhD PostDoc AutreCategorie
%%%
%%% Affiliations possibles :
%%% INRIA CNRS AutreEtablissementPublic UnivFr
%%% UnivEtrangere EtablissementPrive AutreAffiliation
%%%
%%% Utiliser le mot-cl\'e [Habilite] pour les titulaires d'une Th\`ese d'Etat ou d'une HDR
%%%
%%% V\'erifier si les noms sonts corrects
%%%
%%%       \pers{Pr\'enom}{Nom}{profession}{affiliation}[champ_libre: grade, date][Habilite]?
%%%        ...
%%%
%%% La mention "Team leader" est pr\'ecis\'ee dans le champ libre
%%%
%%% Dans ce champ libre, si vous indiquez le grade, \'ecrivez :
%%%      Senior Researcher  pour DR,
%%%      Junior Researcher pour CR ,
%%%      Professor,
%%%      Associate Professor pour Ma\^{\i}tre de conf.
%%%
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%%%
%%% Information sur les donn\'ees import\'ees : membres de l'\'equipe
%%%
%%% Le RAweb 2010 est utilis\'e pour r\'ecup\'erer les membres permanents
%%% de l'\'equipe (c'est \`a dire sans les visiteurs, les postDoc et les divers).
%%%
%%% Url de consulation du RAweb 2010 : http://raweb.inria.fr/rapportsactivite/RA2010/modemic/uid0.html
%%% En cas de probl\`eme avec le RAweb 2010 : raweb-support@inria.fr
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\begin{composition}


% --- permanents
\pers{Fabien}{Campillo}{Chercheur}{INRIA}
     [Senior Researcher, INRIA Sophia-Antipolis]
     [Habilite]
\pers{C\'eline}{Casenave}{Chercheur}{AutreEtablissementPublic}
     [Junior Researcher, since September 2011, INRA]
\pers{Bart}{Haegeman}{Chercheur}{INRIA}
     [Junior Researcher, INRIA Sophia-Antipolis]
\pers{J\'er\^ome}{Harmand}{Chercheur}{AutreEtablissementPublic}
     [Senior Researcher, INRA Narbonne]
     [Habilite]
\pers{Sylvie}{Lelaidier}{Assistant}{INRIA}
     [AI, part time]
\pers{Alain}{Rapaport}{Chercheur}{AutreEtablissementPublic}
  [Team leader; Senior Researcher, INRA Montpellier]
   [Habilite]

% --- Enseignants

\pers{Terence}{Bayen}{Enseignant}{UnivFr}
     [Associate Professor, Universit\'e de Montpellier II]
\pers{Marc}{Joannides}{Enseignant}{UnivFr}
     [Associate Professor, Universit\'e de Montpellier II]
\pers{Claude}{Lobry}{Enseignant}{UnivFr}
     [Professor emeritus, Universit\'e de Nice][Habilite]
\pers{Tewfik}{Sari}{Enseignant}{UnivFr}
     [Professor, Universit\'e de Haute-Alsace; ``d\'el\'egation'' INRIA from February to July; 
     Senior Researcher CEMAGREF (Irstea) Montpellier since October][Habilite]

% --- collaborateurs exterieurs

\pers{Guillaume}{Deffuant}{CollaborateurExterieur}{AutreEtablissementPublic}[Senior Researcher, CEMAGREF (Irstea) Clermont][Habilite]
\pers{Annick}{Lesne}{CollaborateurExterieur}{CNRS}
     [Junior Researcher, LPTMC Paris Jussieu][Habilite]
\pers{Antoine}{Rousseau}{CollaborateurExterieur}{INRIA}
     [Junior Researcher, INRIA-Rh\^one-Alpes]

% --- post-doc
\pers{Chlo\'e}{Deygout}{PostDoc}{AutreEtablissementPublic}
   [ANR DISCO grant]

% --- doc

\pers{Boumediene}{Benyahia}{PhD}{AutreEtablissementPublic}
     [COADVISE grant]
\pers{Amine}{Charfi}{PhD}{AutreEtablissementPublic}
     [COADVISE grant]
\pers{Lamine Mamadou}{Diagne}{PhD}{AutreEtablissementPublic}
     [AUF grant]
\pers{Jos\'e}{Fernandez}{PhD}{AutreEtablissementPublic}
     [CONYCIT/INRIA and INRA grant]
\pers{Coralie}{Fritsch}{PhD}{AutreEtablissementPublic}
     [MESR and INRA grant]
\pers{Amel}{Ghouali}{PhD}{UnivEtrangere}
     [AVERROES grant]
\pers{Ihab}{Haidar}{PhD}{AutreEtablissementPublic}
     [MESR grant]
\pers{Guilherme}{Pimentel}{PhD}{UnivEtrangere}
     [Univ. Mons (Belgique) and INRA grant]
\pers{Angelo}{Raherinirina}{PhD}{UnivEtrangere}
     [University of Fianarantsoa, Madagascar, LIRIMA grant]
\pers{Radhouene}{Fekih-Salem}{PhD}{UnivEtrangere}
     [AVERROES grant]
\pers{Ahlem}{Saddoud}{PhD}{UnivEtrangere}
     [AVERROES grant]
     
     
% --- visiteurs

\pers{Denis}{Dochain}{Visiteur}{UnivEtrangere}    
	[CESAME, Univ. Louvain-la-Neuve (Belgium), 1 month]
\pers{Rampal}{Etienne}{Visiteur}{UnivEtrangere}    
	[Community and Conservation Ecology group University of Groningen (the Netherlands), 1 month]
\pers{Pedro}{Gajardo}{Visiteur}{UnivEtrangere}    
	[Departamento de Matematica, Universidad T\'ecnica Federico Santa Mar\'{\i}a (Chile), 1 week]
\pers{Benjamin}{Ivorra}{Visiteur}{UnivEtrangere}    
	[MOMAT, Univ. Madrid(Spain), 3 weeks]
\pers{David}{Jeison}{Visiteur}{UnivEtrangere}    
	[Universidad de La Frontera (Chile), 1 week]
\pers{Patrick}{De Leenheer}{Visiteur}{UnivEtrangere}    
	[Department of Mathematics, University of Florida (USA), 1 week]
\pers{Hector}{Ramirez}{Visiteur}{UnivEtrangere}    
	[Mathematical Engineering Department, Universidad de Chile, 2 weeks]
\pers{Gonzalo}{Ruiz}{Visiteur}{UnivEtrangere}    
	[Facultad de Ingenier\'\i a, Pontifica Universidad Cat\'olica de Valparaiso (Chile), 1 week]
Mathematical Engineering Department, Universidad de Chile, 2 weeks]
\pers{Alain}{Vande Wouver}{Visiteur}{UnivEtrangere}    
	[Service d'Automatique, Universit\'e de Mons (Belgium), 2 weeks]
\pers{Joshua}{Weitz}{Visiteur}{UnivEtrangere}    
	[School of Biology, Georgia Institute of Technology (USA), 1 week]

% --- stagiaires

\pers{Juan}{Bello Rivas}{AutreCategorie}{UnivEtrangere}    
	[PhD Student,  Univ. Madrid (Spain), 3 weeks]
\pers{Walid}{Bouhafs}{AutreCategorie}{UnivEtrangere}    
	[PhD Student,  Univ. Tunis (Tunisia), 2 months]
\pers{Florent}{Dayle}{AutreCategorie}{UnivFr}    
	[MSc Student,  Montpellier II, 3 months]
\pers{Fadia}{Gafsi}{AutreCategorie}{UnivFr}    
	[MSc Student,  Universit\'e Montpellier II, 5 months]
\pers{Mouna}{Jraou}{AutreCategorie}{UnivEtrangere}    
	[PhD Student,  Univ. Sfax (Tunisia), 3 months]
\pers{Mathieu}{Lauze}{AutreCategorie}{UnivFr}    
	[PhD Student, DUT Universit\'e Montpellier II,  3 months]
\pers{You Zhong}{Liu}{AutreCategorie}{UnivFr}    
	[4th year, INSA Lyon, 2 months]
\pers{Victor}{Riquelme Flores}{AutreCategorie}{UnivEtrangere}    
	[MSc Student,  Universidad de Chile, 2 months]
\pers{Estela}{Tapia}{AutreCategorie}{UnivEtrangere}    
	[PhD Student,  Universidad de Valparaiso (Chile), 3 weeks]

% --- misc.


\end{composition}
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%%%
%%% Section 2. On peut mettre un ou plusieurs modules.
%%%
%%% La partie pr\'esentation du projet est issue de la section "Overall Objectives" du RAweb 2010
%%% source : http://raweb.inria.fr/rapportsactivite/RA2010/modemic/uid0.html
%%%
%%% Si les r\'esultats scientifiques de l'ann\'ee sont importants ou si vous avez re\,cu
%%% des prix li\'es \`a des r\'esultats, alors mentionnez-les dans la rubrique Highlights en fin de la section.
%%%
%%% Si vous avez un "Best paper" r\'ecompens\'e dans une conf\'erence de niveau international,
%%% citez-le en fin de la section Highlights sous la forme : \bestcite{zzzz}, \bestcite{yyyy}.
%%% Vous pouvez en avoir plusieurs.
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%---------------------------------------------------------------------
\begin{module}{presentation}{goal1}{Build, simulate and analyze new models of 
microbial ecosystems}

We investigate different models of microbial ecosystems at different scales, that are related to various research questions for better understanding, predicting or piloting real plants.

Eight families of problems are covering our modelling activities:

\begin{enumerate}

\item study the mathematical properties (equilibriums, stability, limit cycles,
bifurcation...) of macroscopic models that distinguish compartments of attached 
and free bacteria.
We are looking for ecological conclusions in terms of coexistence of species.

\item study the mathematical properties of trajectories of a model
that switches from a populational representation by differential equations to an individual-based model
when the population falls below a given threshold. We expect from this
study new insights on validity domains of macroscopic models and 
quantitative estimations of the variability about average trajectories.

\item build a framework for modelling the chemostat with stochastic
processes at a macroscopic scale justified from hypotheses at the individual
level. The classical ``deterministic'' chemostat is expected to be found as an average dynamics for large populations, but second order moments should provide relevant information about the variability about the deterministic approximation.

\item build and simulate IBMs (individual based models) of 1D biofilm and compare
the spatial densities of biofilm and planktonic biomass with
the numerical solutions of 1D PDE models.

The output of this study is to propose and justify 
attachment/detachment terms in the PDE, that are crucial
in the determination of the thickness of the biofilm, and
that are usually chosen in an heuristic way.


\item compare detailed ecological models of a multi-species community at a fine scale 
with low-complexity models at a coarser scale, in the spirit of
the neutral model. 
Within a stochastically varying environment (that is assumed to have
different impacts on each species), the coarse model could describe 
in an effective way the interaction between species and environment as 
a stochastic variability. The goal is to interpret the parameters of the 
global model in terms of properties of the fine-scale model.

\item study chemostat-like models with multi-resources and nutrient recycling,
within the objective of representing microbial activity in soil ecosystems.
The goal is to understand the influence of the choice of hypotheses about the growth terms (dependency in terms of product or minimum functions of each resource) and the recycling terms (from the dead biomass  or during the division process) on
the qualitative behavior of the system and its performances at steady state.


\item investigate the properties of a network of interconnected chemostats
and understand the role of the size of the nodes and the connectivity (with the help of the VITELBIO software, see Section \ref{module.logiciels.VITELBIO}).


\item couple numerical simulation of fluids dynamics in tanks
with models of biotic/abiotic reactions.
Then, we plan to compare the input/output behavior of these
models with simple representations of networks of 
interconnected chemostats (see the previous point).

\end{enumerate}

\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{presentation}{goal2}{Validate hypotheses and identify models with experimental data}

Among our current collaborations and projects (ANR DIMIMOS, ANR DISCO, ARC VITELBIO, Agropolis Foundation BioInh, RNSC E-MICRAM), we have identified five experimental devices 
that we consider relevant for back and forth exchanges
between models and real-world observations for the coming years.
\begin{enumerate}
\item Molecular fingerprints. The LBE at Narbonne is a world leader for one of these techniques,
the SSCP (Single Strand Conformation Polymorphism) that allows to estimate the 
biodiversity of a microbial ecosystem and serve as a comparison 
instrument over time or between ecosystems. A similar kind of signal from the proteins 
expressions is also obtained within the ANR DIMIMOS with UMR MSE (Dijon).
\item Continuous cultures in chemostats. The chemostat device is the typical investigating device in microbiology. Spatial structures can be mimicked and controlled
using interconnected chemostats. Because of contamination risks, 
experimentation in chemostat requires an adequate expertise which the LBE holds.
We plan to launch new such experiments with microbial populations of
interest for specialists of soil ecosystems, in collaboration
with UMR Eco \& Sols (Montpellier) and UMR Bioemco (Grignon).
\item Taylor-Couette reactors (with LBE Narbonne). These bioreactors are specifically designed for the culture of biofilms on {\em coupons}, that can be removed from the system for static analyses of biodiversity (SSCP) and microscopy. Experiments are already scheduled within a task of the ANR DISCO.
\item Flow-cell bioreactors (with Cemagref Antony). It consists in small capillary tubular reactors continuously fed by a pump, under a microscope that has been
designed for a continuous acquisition of images. 
We aim to compare biofilm models with 
the information provide by theses images.
\item Micro-plates cultures (with UMR MSE Dijon and LBE Narbonne). 
It contains a hundred of small wells in each of which a microbial community 
is grown in batch on the available substrate. 
Optical density measurements allow one to monitor
simultaneously the biomass growth in the wells. These devices are convenient
to study the effects of different initial composition of the community
under the same environmental conditions. We believe that it is also
well suited to test neutral-like community models
\end{enumerate}

Each measurement technique requires its own data analysis 
(filters, statistical analysis, image analysis...) to provide information that are
relevant for the models.
On the basis of these experimental observations, qualitative and quantitative validations of the models will be performed. Observers and image correlations are one of the techniques we are using.
\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{presentation}{goal3}{Propose new strategies to pilot and optimize microbial ecosystems}

We study optimal design and feedback control laws within the framework of 
ongoing projects, and distinguish two kinds of contributions: 

\begin{enumerate}
\item based on already known models of chemostat or fed-batch reactors, but with explicit spatial considerations, for 

\begin{itemize}
\item the biological treatment of natural water resources, one of the main objective of the associated team DYMECOS with Chile, in collaboration with B. Ivorra from MOMAT (Madrid) 
for the numerical computation of the pollutant spreading,
\item the design of interconnected networks of chemostats, in the following of
the former ARC VITELBIO and also in collaboration with the MOMAT for the comparison and identification with hydrodynamics models.
\end{itemize}

\item based on new models developed in the scope of projects:
\begin{itemize}
\item the Euro-Mediterranean project TREASURE coordinated by the team,
where membranes are at the heart of a new generation of bioreactors
of smaller size and well suited for Southern countries where the temperature 
is not too low. 
\item the ANR DISCO 2010-2013, for which one of the output is expected in terms of control of biofilm reactors.
\end{itemize}

\end{enumerate}

We plan to contribute to the development of new decision making tools to design, control, observe and optimize current and future bioprocesses, for the preservation of natural (aquatic or telluric)
resources, where modelling and numerical simulations are clearly expected, and for the biotechnology industry whose objectives are 
to improve efficiency of bioprocesses under constraints of sustainable development
(energetic consumption, biogas production...)

The tools based on geometric and optimal control of nonlinear systems and possibly 
on viability theory should be enhanced by software developments. 
We also expect to contribute to the numerical determination of
optimal feedback laws for a class of problems relevant for the mentioned applications.
\end{module}
%---------------------------------------------------------------------



%---------------------------------------------------------------------
\begin{module}{presentation}{goal4}{Develop a strategy of software production}

Although the software production is not the main objective of the team,
we aim to assemble models within {\em virtual ecosystems}
(with the objective that it can replace or guide real experiments)
in the four coming years.
Co-developments of simulation software will be required within INRIA or outside.
Our working plan is in three steps:
\begin{enumerate}
\item In a first step, we shall develop a {\tt Scilab} 
or {\tt Matlab} toolbox within the team.
\item Simulations of IBM or PDE require significant efforts, in terms of 
computer implementation and numerical methods. 

The main problem in the implementation of IBM's is the size of the population, 
particularly for applications in microbiology where the size of populations of 
bacteria can be very large. In agent-based models (ABM), population sizes are smaller 
and each individual features sophisticated behavior, while in IBM, population sizes are 
usually larger but with limited individual activities. The population size impacts 
both the execution time and the memory size, but the main bottleneck is the execution 
time because of the communication between the individuals. The idea will be to 
propose, through an object-oriented approach, data structures that will limit this 
communication. From the hardware point of view, grid computing could lower execution 
time but only on a limited range.  
New techniques exploiting the architecture of graphics processing units (GPU), that 
are specifically adapted to IBM simulations, seems very promising. These activities will have to be developed in association with 
specialists for our most ambitious developments: MOMAT already cited but also 
possibly the EPI OASIS and researchers from the LIRMM.

\item For the design and development of user-friendly and graphical interfaces
that need to be easily accessible by biologists and bioprocess engineers,
we shall look for the help of service companies 
specialized in agronomy and biotechnology applications (such as ITK Company).
\end{enumerate}

\end{module}
%---------------------------------------------------------------------

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Section Highlights (Faits Marquants)
%%%
%%% Les Highlights (Faits Marquants) doivent \^etre relatifs aux r\'esultats de votre \'equipe.
%%% La bonne mesure pour un fait marquant est l'impact estim\'e. Seuls les r\'esultats scientifiques
%%% importants ou des prix li\'es \`a des r\'esultats justifient la pr\'esence de la rubrique Highlights.
%%%
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%---------------------------------------------------------------------
\begin{module}{presentation}{Highlights}{Highlights}

\begin{itemize}
\item
The Euro-Mediterranean ``3+3'' project TREASURE, conducted by the team, has been renewed for three years.

\item
Our study of interconnections of chemostats with and without diffusion has revealed configurations that can enhance the performances of degradation of microbial ecosystems. The characterization of thresholds on input nutrient concentration and the role of diffusion for such improvements over simpler or well-known configurations are new results. This approach is based on an implicit representation of spatial inhomogeneity in modelling natural soil ecosystems, and brings potential applications for the waste-water industry \cite{HRG11}.

\item
This year the first models of population dynamics taking into account the demographic randomness have been proposed. The random terms in these models are not artificial superimposed on classical ordinary differential equation models but are derived from descriptions at microscopic scales with individual birth and death processes \cite{campillo2011chemostat}. We also proposed some examples where this randomness, even at small intensity, can radically alters  the qualitative behavior of the considered systems \cite{campillo2011h}.


\end{itemize}

\end{module}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Section 3
%%% On a mis un module avec tout ce qu'on peut y mettre.
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%---------------------------------------------------------------------
\begin{module}{fondements}{axis1}{Modelling and simulating microbial ecosystems}

Microbial ecosystems naturally put into play phenomena at different scales, from the individual level at a microscopic scale to the population level at a macroscopic scale, with sometimes intermediate levels. 
The size of substrate molecules is a thousand time smaller than the size of microorganisms and usually diffuse much faster.
The substrate consumption of one microorganism is negligible at the population level but the sum of the consumption of its neighbors can modify the local concentration of substrate, which itself modifies microorganism growth, acting as a {\em feedback loop}.
For other variables that change slowly (pH, temperature...) cumulative effects create 
intermediate time scales, coupling individual and environment dynamics. 
The very large populations justify macroscopic modelling but for some ecosystems, spatial structures seen at intermediate scale need to be tackled. This is typically the case of biofilm ecosystems, for which the biofilm structure is responsible of characteristics of the overall ecosystem.
Models that are purely individual-based or purely populational
are rarely truly satisfactory to incorporate current knowledge on microbial ecosystems at various scales
and to push ahead mathematical analysis or to derive operational rules. 

%......................................................................
\subsubsection{Macroscopic models}

The starting point is the knowledge of biologists that report a large number of mechanisms 
discovered or shown on laboratory experiments at a population level, such as 
competition for a growth-limiting substrate,
predation interactions,
obligate mutualism
or
communication between bacteria.
If each {\em elementary} mechanism is today well understood and modelled at a macroscopic level, 
the consideration of several mechanisms together in a single model is still raising several questions of
understanding and prediction. This is typically the case when there is more than one growth-limiting substrate in the chemostat model or when one couples species competition with a spatial structure (flocculation, niches...).

\begin{enumerate}
\item {\em Non-spatial models.}\\
Ordinary differential equations (ODE) are the common way to describe the evolution of the size or concentration of species populations and their functional contribution in resource transformation (such as substrate degradation) in homogeneous or perfectly mixed compartments (or ecological niches). 
The well-known chemostat model used in microbiology for single strain:
\[
\begin{array}{lll}
 \dot s & = & -\frac{1}{y}\mu(s)b+D(s_{in}-s)\\
 \dot b & = & \mu(s)b -Db
\end{array}
\]
(where $s$ and $b$ stand respectively for the substrate and biomass concentrations), has to be extended to cope with the specificity of microbial ecosystems in the following directions.
\begin{itemize}
\item very large number (hundreds or thousands) of species. This leads to characterization of their distribution during the transients, that is a way to study the {\em functional redundancy} of ecosystems.
\item environmental fluctuations (input flow rate, input concentration, temperature, pH...). This impacts the efficiency of a microbial ecosystem, when biological and environmental time scales are different.
{\em Singular perturbations} is the technique we use to separate {\em slow} 
variables from {\em fast} ones, leading to approximations of the dynamics on {\em slow manifolds} 
to be determined and analyzed.
\item interactions due to several limited resources and trophic chains. Most of the literature on the chemostat considers models with single limited resource, while some work studied purely essential or substitutable resources. 
\item several populations of bacteria (for each species) to describe the effects of certain spatial structures that are artificially created in bioreactors or naturally found in soils, like flocks, colonies or biofilms: the planktonic (or free) cells and the biofilm (or fixed) biomass (for telluric ecosystems, such a distinction
is also relevant to represent the sticking/non sticking characteristics of soil). 
Considering simple models of aggregates (that are not spatialized) can provide a simplified model of the dynamics of the overall biomass. \item {\em active} and {\em dormant} bacteria. This distinction is motivated by the observations made on ecosystems of sparse resources such as arid soils.
\end{itemize}

\item {\em Spatial models.}\\
In the spirit of lattice differential equations,
representations in terms of networks of (abstract) interconnected bioreactors
propose an intermediate level between models 
of average biomass (a single ODE) and a continuous representation of space (PDE).
A model of interconnected bioreactors is a way to {\em implicitly} take into account spatial heterogeneity, without requiring a precise knowledge of it. It is similar to the island models used in ecology but coupled with the dynamics of abiotic resources and hydrodynamics laws (transport, percolation, diffusion) governing the transfers between patches. This approach appears to be relevant for telluric ecosystems, for which pedologists report that microbial activities in soil are usually concentrated in {\em hot-spots} that could be seen as small bioreactors. 
Understanding the role of the topology of the interconnection network
and how a spatial structure impacts the outputs is also relevant in biotechnology to improve the yield or stability of processes.
\end{enumerate}


%......................................................................
\subsubsection{Microscopic models}

In 
these models (birth and death processes, neutral models, individual-based models) 
the dynamic of the population is described in terms of discrete events: birth and 
death of individuals, or jumps in terms of biomass. These models can be gathered 
under the same framework that could be called \emph{Markov stochastic processes 
with discrete events}. Most of the time they should be coupled with continuous 
components like the size of each individual or the dynamic of the resources 
(represented in terms of ODE or PDE).

The Markovian framework allows on the one 
hand sharp analyses and rescaling techniques; on the other hand it 
induces a simplification in the memory structure that is important in terms of 
simulation. Indeed, as the future state of the system depends from the past only 
through the present state, only the current state should be kept in memory for 
simulation.

We will consider three families of processes with discrete events, from simplest 
to most complex.
\begin{enumerate}
\item[-] {\em Birth and death processes.}\\
 These models are of first importance in small 
population size. They indeed allow investigation of near-to-extinction situations in 
a more realistic ways than the classical ODE models: they permit the computation, 
analytically but most of the time numerically, the distribution of extinction time 
and the probability of extinction.  Efforts should be made to developed efficient 
Monte Carlo simulation procedures and approximation techniques for extinction 
probability and time distribution evaluation. In larger population sizes, they are 
advantageously approximated by diffusion models (see next section).

\item[-] {\em The neutral models.}\\
 In {\em neutral} models sizes of different species 
evolve as birth and death processes with immigration: all individuals have the same 
characteristics and are not spatialized. Such hypotheses could be considered 
unrealistic from a purely biological perspective, but these models focus on some 
precise properties to be simulated and predicted (for instance the biodiversity).

Comparing the prediction of species abundance of 
these models to real observations provides a way to justify or invalidate the 
neutral hypothesis.
Extensions of the neutral model, that was 
originally introduced for forest ecology, have to be developed in order to better suit the framework of 
microbial ecology, such as the non constant size of the populations and spatialized variations.

\item[-] {\em The individual based models.}\\
 IBM's appear to be well suited to describe 
colonies or biofilms: in addition to birth, death and movement events, one has to 
consider {\em aggregation} and {\em detachment} events. The mechanisms that lead to 
the emergence of spatial patterns of colonies, or the formation of biofilms, which 
adhere to surface via polymers generated by the bacteria under specific hydrodynamics 
conditions, are not well understood yet. Typically, one can consider that bacteria 
inside the aggregates are disadvantaged to access the nutrient.

\end{enumerate} 

IBM modelling is a convenient way to propose aggregation and detachment mechanisms at 
the individual level in terms of random events connected to the geometry of the 
neighborhood, and to compare generated images with microscopic observations 
(for instance the confocal microscopy).

One has to be aware that few methods are available to study systematically and rigorously the properties of IBM, contrary to models based on differential equations (ODE, PDE...).

%......................................................................
\subsubsection{Bridges between models}

The ``theory of a computational model'', that combine two kinds of models (typically ODE and IBM)
that are different representations of the same objects,
relies on two steps: the ``program making'' and the ``theoretical study'', in the spirit of the {\em double modelling} approach (roughly speaking, it consists in grasping the complexity of a IBM by analyzing accurately the consequences of each hypothesis on the macroscopic behavior of the model, building an approximate model of its global dynamics).
Two main tools can be considered.

\begin{enumerate}
\item {\em Change of scale.}\\
For IBM models (neutral or Markovian), we consider mean field and moments approximation techniques 
that provide 
information at the macroscopic (i.e. populational) level, to be compared with macroscopic models.
From a birth-and-death process  describing the individual level, a renormalisation can provide a stochastic differential equation at a meso-scale.
The {\em diffusion approximation} technique can be understood as a numerical acceleration technique where the number of births and deaths follows a normal law.
These stochastic models at meso-scale can provide additional information compared to deterministic models at a macro-scale, such as parameter identifiability or finite time extinction.
The price to pay is to give much more conceptual and numerical efforts,
 that become less relevant for very large populations.\\
For PDE models on spatial domains described with regular patterns (such as models of biofilm), the homogenization technique allows to obtain simpler PDE with constant parameters.

\item {\em The multi-scale modelling.}\\

The spatial heterogeneity in microbial ecosystems require to consider 
simultaneously several scales:
\begin{itemize}
\item[-] a {\em physical} scale. In batch processes, nutrient diffusion can be modelled by adapting the heat equation with Dirichlet boundary conditions. In continuous reactors, a convection-diffusion equation with Neumann boundary conditions is considered instead, the speed vector field being provided by the equations of fluid mechanics.
The spatial scale used for the discretization is given by diffusion and flow 
parameters.
\item[-] a {\em biological} scale, given by the size and mobility of bacteria. Usually, 
this scale is larger than the physical one (at least in the liquid phase).
\item[-] an {\em aggregation} scale of colonies or biofilms, even larger, that provides the
spatial patterns.
\end{itemize}
Theoretically, it is always possible to describe all the processes at the smaller common scale
and then use particle methods for a global representation, but this leads to extremely long computation times. The challenge is to manage these overlapping scales together and guarantee the stability of the numerical schemes. This is the goal of the {\em multi-scale} approaches. For microbial ecosystems, it consists in 
\begin{enumerate}
\item proposing new representations of the various scales of aggregation of bacteria in a model, taking into account the attachment-detachment processes determined by the local hydrodynamics conditions. Here, discussions with specialists of fluid mechanics are required.

\item coupling diversity models (e.g. models based on the neutral assumption) with spatial models 
(that reproduce the patterns observed on images of microscopy) to better understand the link biodiversity/structure.

\item introducing new  {\em control} variables, considered as independent variables, each of them describing a proper scale. For this purpose, we investigate different techniques available to determine such variables: 
{\em mean-field} approximation,
{\em singular perturbations}, 
{\em unification by limiting layers} 
or
\item {\em renormalising}, that  aims at detecting invariants among models of different scales.

\end{enumerate}
\end{enumerate}


\end{module}
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%---------------------------------------------------------------------
\begin{module}{fondements}{axis2}{Interpreting and analyzing experimental observations}

The validation of microbial models on data is rarely a straightforward task, because observations are most of the time not directly related to the variables of the models.
Techniques such as abundance spectrum provided by molecular biology or confocal imagery are relatively recent in the field of microbial ecosystems. The signals provided  
by theses devices leave many research questions open in terms of
data interpretation and experiments design. 
One can distinguish three kinds of key information that are needed at the basis of model assumptions:
\begin{itemize}
\item[-] structure of the communities (i.e. who is present?),
\item[-] nature of interactions between species (competition, mutualism, syntrophism...),
\item[-] spatial structure of the ecosystems.
\end{itemize}

%......................................................................
\subsubsection{Assessment of community structures}


Ecosystems biodiversity can be observed at different levels, depending on the kind of observations. One usually distinguish
\begin{enumerate}
\item {\em The taxonomic diversity.} 
Several techniques developed by molecular biologists
can gather information on the genetic structure of communities:
\begin{itemize}
\item {\em sequencing of a given gene in the community.} The RNA 16S gene is often chosen
to identify bacteria or Archeae.
\item {\em molecular fingerprints.} Some regions in the sequence of the RNA 16S gene
encode faithfully the taxa species and can be amplified by PCR techniques. 
\item  {\em the sequencing of the overall genetic material of a community} (meta-genomic)\end{itemize}
All these techniques bring new problems of data interpretation to estimate in a robust manner the properties of communities. The signals are combinations of contributions of abundances from each taxon. For an ecosystem with a limited diversity, composed of known species, the signal allows to determine with no ambiguity the abundances. In natural ecosystems, the signal is more complex and it is hopeless to determine uniquely the taxa distribution.

\item {\em The functional diversity.} It is usually observed at a larger scale, measuring the performances of the overall ecosystem to convert organic matter.
The taxonomic diversity does not usually provide such information (it is possible to study {\em functional genes} but this is much more difficult than studying the 16S one).

A convenient way to study the functional performance of microbial
community dynamics is to grow the same microbial community on
different substrate compositions, and monitor its performance on these different substrates. 
Neutral community models provide a reference for what would
happen if no functional differences are present in the community. The
deviation of experimental observations from neutral model predictions
can be considered as a measure of functional diversity.

\end{enumerate}

Understanding the links between taxonomic and functional diversity
is currently a tremendous research question in biology 
about genotype/phenotype links, that one can also find in the specific context of microbial ecosystems.

%......................................................................
\subsubsection{Characterization of the interactions}

The role of biodiversity and its preservation in ecosystems are research questions 
currently largely open in ecology. The nature and number of interactions 
between bacterial populations are poorly known, and are most probably a key to understand biodiversity. In the classical chemostat model, inter-specific interactions are rarely considered. In theoretical ecology, interaction information is typically encoded in an {\em interaction matrix}, but the coupling with common abiotic resources and the stoichiometry is rarely considered in the models.

The information provided by confocal microscopy is also a way to estimate the distance of interactions between microorganisms and substrates. This knowledge is rarely documented although it is crucial for the construction of IBM.




%......................................................................
\subsubsection{Observation of spatial structures}

Schematically, one can distinguish two origins of spatialization:

\begin{enumerate}
\item due the physics of the environment. In bioprocesses, this happens typically for 
large tank size (inducing {\em dead zones}) or sludge accumulation making the suspension closer from a porous medium than a liquid one.
Numerical experimentation can be driven, coupling a
solver of the equations of the fluid mechanics with microbiology equations. Then, the spatial distribution of the biomass can be observed and used to calibrate simpler models.  
Typically, a dead zone is modelled as a
diffusive interconnection between two perfect (abstract) tanks.

But the biotechnology industry aims at considering more sophisticated devices than simple tanks. For instance, the fluidized bed technique consists in creating a counter-current with oxygen bubbles for preventing the biomass to leave the rector. 
In more complex systems, such as soil ecosystems, 
it is difficult to obtain faithful simulations because
the spatial structure is rarely known with accuracy. Nevertheless,
local observations at the level of pores can be achieved,
providing information for the construction of models. 
\item due to the formation of aggregates (flocks, biofilms...) or biomass wall attachment.
Patterns (from ten to a hundred micro-meters) can be observed with 
confocal microscopy.

Spatial distribution of bacteria, shape of patterns and composition of the aggregates help express hypotheses 
on individual behaviors.
But quantification and variability of images provided by confocal 
microscopy are difficult. An open question is to determine the relevant morphological indicators that characterize aggregation and
the formation of biofilms.
\end{enumerate}

\end{module}
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%---------------------------------------------------------------------
\begin{module}{fondements}{axis3}{Identifying, controlling and optimizing bioprocesses}

The dynamics of the microbial models possess specificities that do not allow the application of the popular methods of the theory of automatic control, such as linear control, feedback linearization or canonical forms.
\begin{itemize}
\item[-] positivity constraints. State variables, as well as control inputs, have to stay non-negative (input flow pump cannot be reversed because of contamination issues).
\item[-] non-linearity. Several models have non-controllable or non-observable linearizations when inhibition effects are present (i.e. change of monotonicity in the growth curves).
\item[-] model and measurement uncertainties. In biology, it is rarely relevant to consider model uncertainties as additive Gaussian or finite energy signals.
\end{itemize}

%......................................................................
\subsubsection{Software sensors and identification}

Sensors in biology are often poor and do not provide the measurements
of all the state variables of the models: substrate, strain and product
concentrations.
In addition, measurements are often spoilt by errors. For instance
optical density measurements give an indirect measure of the biomass, influenced by abiotic factors that share the same medium. 

Analytical techniques are well suited to ODE models of small dimension, such as:
\begin{itemize}
\item[-] guaranteed set-membership observers, when the system is non observable or in presence of unknown inputs,
\item[-] (non-linear) changes of coordinates, when the system is observable but not in a canonical form for the construction of observers with exponential convergence. 
\end{itemize}

Software sensors can be also derived with the help of simulation based approaches like particle filtering techniques. This method is suited to diffusion models that approximate birth and death processes. They will allow us to investigate the different sources of randomness: demography, environment, but mainly imprecision of the sensors.

Similarly, identification techniques for constant parameters are based on sensor models as well as demography and environmental randomness models. In this case, Bayesian and non-Bayesian statistical techniques can be used. 

%......................................................................
\subsubsection{Bioprocess stabilization}

In bioprocesses, the most efficient bacterial species at steady state
are often inhibited by too large concentrations of substrates (this corresponds
to assuming that the growth function  $S \mapsto \mu(S)$ in the
classical chemostat model is non-monotonic).
This implies that the washout equilibrium
(i.e. disappearance of the biomass) can be attractive, making the bioprocess bi-stable.

A common way to globally stabilize the dynamics toward
the efficient equilibrium is to manipulate the dilution rate $D$. But a diminution of the input flow rate for the stabilization
requires to have enough room for an upstream storage, which is an expensive solution
especially for developing countries that need to be equipped with new installations.

Alternative ways are proposed to stabilize bioprocesses 
without restricting the input flow rate:
\begin{itemize}
\item[-] either by {\em physical means}, in terms of recirculation and bypass loops, or membranes as a selective way to keep bacteria and their aggregates inside the tank
and improve its efficiency. 
\item[-] either by {\em biological means}. The {\em biological control}
consists in adding a small quantity another species with particular growth characteristics, that will help the other species to win the competition in the end.
\end{itemize}


%......................................................................
\subsubsection{Optimal control of bioreactors}

The filling stage of bioreactors, or ``fed-batch'', is often time consuming 
because the quantity of initial biomass is small and consequently the population 
growth is slow. The minimal time is a typical criterion for designing a filling strategy, but the optimal feedback synthesis is non trivial and may present singular arcs 
when the growth function is non-monotonic. 

Recent progress have been made in the consideration of
\begin{itemize}
\item[-] multi-species in sequential reactors (having more than one strain makes significantly more difficult to analyze singular arcs because of the higher dimensions of the state space, and there is little literature on the subject),
\item[-] energy consumption of flow pumps and the value of byproducts of the biological reactions such as biogas in the criterion (instead of minimal time or as penalties).
Recent concerns about sustainable development encourage engineers to look for compromises between those objectives under constraints on output concentrations.
\end{itemize}

%......................................................................
\subsubsection{Plant design and optimization}

We distinguish two kind of setups:

\begin{enumerate}
\item {\em The industrial setup.}
A research question, largely open today, is to identify networks of  interconnections of bioreactors that are the most relevant for industrial applications in terms of the following objectives:
\begin{itemize}
\item reasonably simple configurations (i.e. with a limited number of tanks and connections),
\item significant improvement of the residence time at steady state over single or simpler configurations, or shapes of the reservoirs such that the total volume required for a given desired conversion factor at steady state is reduced.
\end{itemize}

\item {\em The bioremediation setup.}
Typically, the concentration of pollutant in a natural reservoir is solution of a transport-diffusion PDE,
but the optimal control of the transport term is  almost not studied in the literature.

An approach consists in finding satisfactory approximations of the solutions of transport-diffusion-reaction PDE (for which the Eulerian speed of the fluid is determined by the Navier \& Stokes equation), in terms of a network of ODEs, that makes effective the application of the Pontryagin Maximum Principe.
\end{enumerate}

\end{module}
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%%% Section domaines d'applications
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\begin{module}{domaine}{water}{Preservation of water resources}

The biological decontamination of wastewater is our main application domain,
in the continuation of the long collaboration 
with the INRA research laboratory LBE. 
We target applications from the decontamination industry, held by large groups
as well as small companies specialized in specific pollutants (for instance in 
fish farming). We aim also to study connected application domains for
\begin{itemize} 
\item[-] the aquatic ecology where microorganisms 
play an important role in the quality of natural water resources,
\item[-] the re-use of water in arid climates for countries of North of Africa, within the euro-Mediterranean project TREASURE.
\end{itemize}

\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{domaine}{soil}{Microbial ecology of soil}

This application domain is more recent for the team members. We target
\begin{itemize}
\item[-] the research questions raised by agronomists, about the better understanding of the interactions and the biodiversity of microbial communities in soils,
with the help of models and numerical simulations,
\item[-] the role of spatial structures on the functions or
{\em ecological services} of microbial ecosystems, notably the soil fertility 
and the carbon sequestration.
\end{itemize}
\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{domaine}{fermentation}{Control of fermentation processes}


Very closely to our studies about wastewater bioreactors and chemostat models, 
we target applications in fermentation processes:
\begin{itemize}
\item[-] either for agro-food products. A typical application is the control
of  cascade fermenters in the study of wine fermentation with UMR SPO 
(Montpellier), within the European project CAFE.
\item[-] either for the green chemistry.
A typical application is the consideration of spatialization in enzymatic models 
of production of agro-polymers with UMR IATE (Montpellier), within the BioInh project financed by Agropolis Foundation.
\end{itemize}
\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{domaine}{digestion}{Animal digestive ecosystem}


Ruminants absorb plant cells, mainly constituted by cellulose, from which 
the microbial population of their digestive system  
extracts carbon and energy to provide proteins and energetic molecules.
This bio-conversion produces also important quantities of methane, a gas responsible of part of the greenhouse effect (the billion of cows on earth 
reject 20\% of the methane linked to human activities). INRA researchers
have shown that this methane production could be reduced by  
30\%\ by changing the proportion of fat acids in the their food, that
also implies that the composition of their microbial ecosystem is modified.

This application domain of the microbial ecology is at an early stage.
URH team (Clermont) has developed an artificial rumen that is close to a chemostat, for testing different kind of nutrition diets.
Preliminaries contacts have been taken, and a modelling demand has been clearly formulated. This theme falls into the research priorities for
the environment preservation.

\end{module}
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%%% Section logiciels.
%%% Il y a plein de modules ici
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%%%
%%% Information sur les donn\'ees import\'ees : logiciels
%%%
%%% Les logiciels pr\'esent\'es ici sont export\'es depuis la base Inria des logiciels (BIL)
%%% La BIL \'etant en cours de d\'eveloppement, il n'y a pas encore d'interface
%%% de d\'ep\^ot ou de modification de logiciels.
%%%
%%% Url de consulation : http://bil.inria.fr/
%%% En cas de probl\`eme BIL : raweb-support@inria.fr
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%---------------------------------------------------------------------
\begin{module}{logiciels}{VITELBIO}{VITELBIO}
\label{module.logiciels.VITELBIO}

\begin{participants}
\pers{J\'er\^ome}{Harmand}, \pers{Alain}{Rapaport}
\end{participants}
VITELBIO (VIrtual TELluric BIOreactors) is a simulation tool for studying networks of interconnected chemostats with the objective of mimicking microbial activities in soil.
The software, developed with the
help of ITK Company, is accessible on a server from any web navigator
and make use of Flex for the user interface and Octave for the numerical integration.
An important effort has been made for obtaining a pleasant
and easy interface that is appealing for microbiologists: the network
can be drawn graphically on the screen and simulation results
can be easily compared between (virtual) experiments, superposing
trajectories curves.

This software is used by several researchers,  from LBE (INRA Narbonne), UMR Eco \& Sols (Montpellier), UREP (INRA Theix), Biomeco (Paris-Grignon), UMR EGC (Paris-Grignon).... and also as a teaching support.
See the web page \url{http://sites.google.com/site/vitelbio/}

\end{module}
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%%%
%%% Section Resultat nouveaux
%%% Il y a plein de modules ici
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%---------------------------------------------------------------------
\begin{module}{resultats}{theory}{Theoretical results}

%......................................................................
\subsubsection{Theory of competition for one limited resource}
\label{competition}
\label{one_resource}

\begin{participants}
	\pers{Claude}{Lobry}
 	\pers{Tewfik}{Sari}
 	\pers{Radhouene}{Fekih-Salem},
\end{participants}



In the paper \cite{sari2011a},
we give a global asymptotic stability result for a mathematical model of
competition between several species in a chemostat, by using a new Lyapunov function. The model includes
both monotone and non-monotone response functions, distinct removal rates for the species and variable
yields, depending on the concentration of substrate.

In the paper \cite{fekih-salem2011a}, we consider the mathematical model of two species microbial competition
on a single food resource in a chemostat, when one takes into account species interactions between the two
populations of microorganisms and intraspecific interactions between individuals themselves, using strictly
monotonic growth functions and distinct dilution rates.



%......................................................................
\subsubsection{Study of input/output maps of interconnected chemostats}

\begin{participants}
	\pers{Alain}{Rapaport}, 
	\pers{Ihab}{Haidar}
\end{participants}


Patch or island models are popular in ecology, and are a convenient way to study the influence of a spatial structure of a geography on the distribution of the abundance of resources. Coupling such a structure with abiotic/biotic models and studying its input-output properties has been very rarely tackled in the literature.
In biotechnology engineering, dead-zones models, that distinguish two sub-domains (a ``living'' and a ``dead'' one) are often used for approximating non perfectly mixed tanks. No more sophisticated representation, apart continuous space models (systems of partial differential equations), have been investigated.

We consider an hydric capacity and an nutrient flow that are fixed, and analyze the influence
of different structures, having the same total hydric volume, on the output concentrations at steady-state,
Three configurations are compared, under the assumption of a monotonic growth rate:
perfectly-mixed, serial and parallel with diffusion rate. In each case, we show the uniqueness of a steady-state different to the washout equilibrium and its global asymptotic stability in the positive orthant. We prove the existence of a threshold on the input
concentration of nutrient for which the benefits of the serial and parallel configurations over the perfectly-mixed
one are reversed. In addition, we show that the dependency of the output concentrations on the
diffusion rate can be non-monotonic, and give precise conditions for the diffusion effect to be advantageous \cite{HRG11,HRG11c}.
The study encompasses the dead-zone models.

The possibly non-monotonic influence of the diffusion parameter on the output steady state
is not intuitive, and leaves further investigations open for understanding or taking benefit of this property
for natural ecosystems (such as saturated soils or wetlands) as well as for bioprocesses (such as wastewater
treatments). This result can be also of interest for reverse engineering when deciding which among serial or
parallel configurations is a better fit for the modelling of chemostat-like ecosystems, providing that one has an
estimation of the hydric capacity of the system.

This work is part of the material thesis of I. Haidar \cite{HaidarThesis}.



%......................................................................
\subsubsection{Aggregation models in the chemostat}

\begin{participants}
	\pers{Claude}{Lobry}
	\pers{Alain}{Rapaport}
	\pers{J\'er\^ome}{Harmand},
 	\pers{Tewfik}{Sari}
 	\pers{Radhouene}{Fekih-Salem},
\end{participants}

Bacteria aggregation often occurs in bioprocesses, creating flocks or biofilms (the latter being attached to the tank walls). At a macroscopic level with large populations of aggregated and non-aggregated individuals, a simple way of modelling this phenomenon in the chemostat is to distinguish explicitly two populations: planktonic or free bacteria and attached ones. The main differences between flocks and biofilms rely in the attachment/detachment terms and the effective dilution rate (assumed to be zero or very small for biofilms). Typically, the specific growth rate of free bacteria is expected to be larger than the attached one (that have in average a restricted access to nutrient and use part of their energy to glue together).

Based on former works of the team and the main assumption that attachment and detachment dynamics are much faster than the biological one, we have shown that a significant difference between the specific dilution rates of the free and attached populations can surprisingly lead to bi-stability, even for a single species and monotonic growth rates \cite{fekih-salem2011c,FEKIHSALEM:2011:HAL-00604633:2,fekihsalem2011a-arxiv}.

A work in progress addresses the case of two species, one of them having a non-monotonic growth rate (due to substrate inhibition) and the ability to form flocks. Without flock, the Competitive Exclusion Principle extended by G. Wolkowicz and her co-authors \footcite{Wolkowicz1985} shows the possibility to have one of the two species winning the competition depending on their initial repartition. Here, the presence of flocks may lead on the contrary to a single winner.




%......................................................................
\subsubsection{Neutral community models for microbial ecology}
\label{SectionNeutral}

\begin{participants}
	\pers{Bart}{Haegeman}
\end{participants}

Hubbell's neutral model \footcite{hubbell2001a} describes the dynamics
of an ecological community in terms of random birth, death, immigration
and speciation events, attributing equivalent characteristics to all
species. Despite the absurd simplicity of these assumptions,
remarkable agreement between neutral model predictions (e.g., the
distribution of the abundance of the species present in the community)
and empirical observations has been reported for some, mostly rather
diverse, ecological communities.

There is some evidence that also certain aspects of microbial
communities can be well described by the neutral model. Highly diverse
microbial communities have been difficult to deal with using more
traditional modelling approaches from community ecology. The neutrality
assumption could lead to an effective global description, without
requiring quantitative species data (growth characteristics,
interaction strengths, etc). We are actively participating in the
development of neutral community models, with a focus on microbial
systems.

(1) Effect of speciation process

It has been argued that the neutral model predictions are rather
insensitive to its assumptions. However, we have found that the
details of the way new species appear in the community (i.e., the
speciation process) do matter, and can drastically change the model
predictions. In particular, we have studied the neutral community
model with random fission speciation. This speciation model is quite
different from the point mutation model usually considered in neutral
community model, and is generally believed to be more realistic.

Using a technique from theoretical physics, we have obtained
the stationary distribution of species abundances for
the random fission model. We have compared our solution with the
well-known stationary distribution of species abundances for the point
mutation model on empirical data (tree communities in tropical
forests) \cite{etienne2011}. Surprisingly, we found that the point
mutation model fits the data better than the random fission model,
although the latter is believed to be more realistic.

(2) Comparison with niche models

Neutral community models challenge more traditional, niche-based
models in community ecology. Niche theory states that species can
coexist only if they differ sufficiently in their characteristics (for
example, their use of available substrates). Neutral theory assumes
that all species have approximately equal characteristics. Hence, the
two theories describe species coexistence in fundamentally different
ways.

We have tried to narrow the gap between the two theories.  We have
proposed a mathematical model that combines essential features of
niche-based and neutral community models \cite{haegeman2011a}.
It integrates species niches, described as Lotka-Volterra interactions,
in the standard neutral community model.  The analysis of this model
indicates that the addition of species interactions has a limited
effect on the species abundance distribution.  We have further clarified
this result using a slightly different model that also combines niche
and neutral features \cite{haegeman2011b}.  For the latter model
we have proved that the niche structure does not affect at all the
species abundance distribution.


%......................................................................
\subsubsection{Quantifying genetic diversity of bacteria}
\label{SectionGeneDiv}

\begin{participants}
	\pers{Bart}{Haegeman}
\end{participants}

With the wide availability of DNA sequencing, microbiologists are
now able to rapidly sequence entire bacterial genomes.  Comparison
of these genomes has revealed a large genetic diversity within
bacterial species.  For example, one genome of the bacteria
\emph{E.~coli} has about 4000 different genes, but a set of 10
genomes of \emph{E.~coli} has typically over 10000 different genes.
Some of these genes are shared by all or almost all of the genomes,
but many other genes are only present in one or a few of the genomes.
This observation has important implications for the definition of
bacterial species and for the description of the functional
characteristics of bacteria.

We have been dealing with the problem of how to quantify this observed
genetic diversity.  Microbiologists have introduced notions like the
pan genome of a bacterial species (that is, the set of genes that
are present in at least one of the organisms from the species)
and the core genome of a bacterial species (that is, the set of genes
that are present in every organisms from the species). However,
we have argued that both the pan and core genome are difficult to
estimate, and should not be used for quantitative purposes
\cite{kislyuk2011}.  Instead, we have proposed a measure of genetic
diversity that has much better estimation properties.  It is based
on the average number of genes shared by a pair of genomes when
sequencing two randomly sampled organisms from the species under
consideration.  We have applied our estimator on six bacterial species
(about 100 sequenced genomes in total).  Software for our robust
estimation procedure of genetic diversity is freely available, see
\url{http://ecotheory.biology.gatech.edu/downloads/genomic-fluidity-scripts}.


%......................................................................
\subsubsection{Individual-based modelling}
\label{sec.recent.ibm}

\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Chlo\' e}{Deygout},
	\pers{Coralie}{Fritsch},
	\pers{Marc}{Joannides},
	\pers{Claude}{Lobry}
\end{participants}


In terms of computational modelling of ecosystems, individual-based models (IBMs) are an interesting path to explore. We can outline two types of IBMs. On the one hand ``detailed IBM'' attempt to integrate in an ad-hoc way all the knowledge available about an ecosystem. On the other hand, ``simplified IBM'' are limited to one or several mechanisms to simplify the analysis. The former may be more realistic but are often difficult to analyze. Although the latter are too simplistic in realistic situations they lend themselves to the analysis and numerical analysis. We focus on the latter.

The IBMs offer an interdisciplinary language between biologists, biotechnologists, mathematicians, and computer scientists, to develop models in the form of relatively simple rules. In the case of simplified IBMs it is possible to translate these rules in the form of a branching Markov process with values in a space of measures. Using scaling methods, the IBMs can be approximated by integro-differential equations; using model simplification methods IBMs can be reduced to stochastic or ordinary differential equations. The mathematical interpretation of the IBMs and their analysis is relatively recent and still very few studies exist \footcite{fournier2004a}. The numerical analysis of these models is yet to be built. Under certain conditions, IBMs themselves can be simulated through adapted Monte Carlo procedures.

The MODEMIC project-team develops three studies in the field of IBMs. The first is part of the ANR MODECOL on the modelling of clonal plant growth
(see Section \ref{module.contrats.modecol});
the second is part of the ANR DISCO on modelling of biofilms
(see Section \ref{module.contrats.DISCO}), the last one is a starting thesis.

In all cases, we aim at developing the Monte Carlo simulation of the IBM as well as analyzing their links with integro-differential models. We also seek to make connections with non-IBM models proposed in
Section \ref{sec.recent.stochmodel}.


In October 2011, Coralie Fritsch started a thesis at the \'Ecole Doctorale I2E of the University of Montpellier 2, under the supervision of Fabien Campillo,
J\'er\^ome Harmand and Marc Joannides. This thesis is supported by a grant of the MESR and a grant of INRA
from the MEM Meta-program (M\'eta-omiques des \'ecosyst\`emes microbiens). The thesis aims at developing and analyzing individual-based microbial ecosystems models that  capture both the spatial, biodiversity and function of these ecosystems. The thesis received the \href{http://www.agreenium.org/}{Agreenium} label in December.


%......................................................................
\subsubsection{Hybrid modelling of biofilms in plug-flow reactors}
\label{sec.ibm.disco}

\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Chlo\' e}{Deygout},
	\pers{Annick}{Lesne},
	\pers{Alain}{Rapaport}
\end{participants}

Within the DISCO project of the SYSCOMM program founded by the ANR,
we have proposed a multi-scaled modelling that combines three scales: a microscopic one for the individual bacteria, a mesoscopic or ``coarse-grained'' one
that homogenises at an intermediate scale the quantities relevant to the attachment/detachment process, and a macroscopic one in terms of substrate
concentration (see the Section \ref{module.contrats.DISCO}).

Such an ``hybrid'' approach allows for modelling and understanding in plug-flow reactors \cite{DEYGOUT:2011:HAL-00604377:1} the interplay between
\begin{itemize}
\item[-] the formation of the biofilm at a microscopic scale, that starts from a small number of bacteria (thus a stochastic individual based description),
\item[-]  the limitation of the biofilm, due the carrying capacity of the wall attachment, at a mesoscopic scale,
\item[-]  the consumption of nutrient along the flow at a macroscopic level, as a solution of a coupled transport-reaction partial differential equation.
\end{itemize}
The numerical computation of such a model requires a software architecture that allows the simultaneous simulation of stochastic events at the bacteria scale and the continuous evolution (in space an time) of the substrate density.

Experiments on real tubular plug-flow reactors are currently driven  at Cemagref HBAN with the perspective of comparison with numerical simulations. After spending one year at Montpellier for deriving and simulating the theoretical model, our post-doctoral fellow C. Deygout is presently participating to the real experiments at Cemagref Antony.

The multi-species case with different bacteria specialized in different environments (poor or rich in nutrient) is a work in progress.


%......................................................................
\subsubsection{Stochastic modelling for biotechnology}
\label{sec.recent.stochmodel}

\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Marc}{Joannides},
	\pers{Claude}{Lobry}
\end{participants}

This year we continue to study stochastic models for the chemostat \cite{campillo2011chemostat,campillo2011e,campillo2011g}.
Starting from the well-known ordinary differential equation systems, we propose first a pure jump process model at the microscopic scale that leads to a stochastic differential equation at the intermediate scale and to an ordinary differential equation at the macroscopic level (fluid limit model). After developing the model, we establish the Fokker-Planck partial differential equation for the diffusion model. This PDE integrates a specific washing-out term. We proposed an ad hoc numerical integration scheme for the simulation of this PDE \cite{campillo2011e}.


In \cite{campillo2011h}, we consider a stochastic version of the basic predator-prey differential equation model. The model, which contains a parameter $\omega$ which represents the number of individuals for one unit of prey -- if $x$ denotes the quantity of prey in the differential equation model $x = 1$ means that there are $\omega$ individuals in the discontinuous one -- is derived from the classical birth and death process. It is shown by the mean of simulations and explained by a mathematical analysis based on results in singular perturbation theory (the so called theory of Canards) that qualitative properties of the model like persistence or extinction are dramatically sensitive to $\omega$. For instance, in our example, if $\omega = 10^7$ we have extinction and if $\omega = 10^8$ we have persistence. This means that we must be very cautious when we use continuous variables in place of jump processes in dynamic population modelling even when we use stochastic differential equations in place of deterministic ones.

%......................................................................
\subsubsection{Minimal time control of batch bioprocesses}
\begin{participants}
	\pers{Denis}{Dochain},
	\pers{Alain}{Rapaport}
\end{participants}

Minimal time control problems often occur in biotechnology when one has to fill tanks. Typically, the objective to be reached is to have the tank full with a prescribed value of substrate or product concentrations, the tank being filled with a high concentration of nutrient.

When a single reaction occurs, the optimal solution is already known and has been rigorously proved by. J. Moreno in 1999 \footcite{Moreno1999} using the Green's theorem: it consists in a ``bang-bang'' strategy (fill as fast as possible or do not fill) and possibly a singular arc when the growth function presents an inhibition (i.e. a maximum growth for a precise concentration of nutrient). When impulse controls in addition to regular control are allowed, an extension of this result has been recently proposed with a different technique that do not use the Green's theorem \refercite{gajardo2008refer}. This technique has also allowed to solve partially the problems when several species compete for the degradation of the substrate, but when all of them have a monotonic growth.

In the presence of complex non monotonic kinetics, typically characterized by the combination of two non-monotonic growth functions, aimed at emphasizing the presence of two parallel metabolic pathways to transform the limiting substrate into the biomass, the candidate singular
arcs are multiple and determining which singular arc is eventually
optimal is clearly a crucial issue. The local optimality conditions
based on the Pontryagin Maximum Principle allow to characterize
the geometric structure of the extremal trajectories, in which there may be singular arcs, but these necessary conditions are not always sufficient for determining which extremals are (globally) optimal.
Then one has to compute the cost of each extremal or use global
optimization methods such as dynamic programming or Hamilton-Jacobi-
Bellman equation. The extremals are traditionally determined
numerically by considering shooting methods, but for bang-bang control, it is well known that one may face numerical
troubles because the shooting function is in general not smooth.
For this problem, we have used an approximation technique first proposed and studied by C. Lobry and his students \footcite{Spinelli1988} and later by C. Silva and E. Tr\'elat \footcite{SilvaTrelat2010}, that consist in adding an artificial control.
In \cite{RD11}, we have proposed a new proof of convergence based on differential inclusions arguments that allows to relax the assumption of the uniqueness of the optimal solution for the convergence of the optimal paths of \footcite{SilvaTrelat2010}. Then we have shown how to apply numerically this approximation procedure for analyzing the field of extremals on the whole state space. This technique appears to be quite effective for the practical determination of optimal synthesis in the planar case even in presence of multiple singular arcs.


%......................................................................
\subsubsection{Optimal control of continuous bioprocesses}
\label{optimal-control}
\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}, 
	\pers{Jos\'e}{Fernandez}, 
	\pers{Walid}{Bouhafs}, 
	\pers{Amel}{Ghouali}
\end{participants}

In continuous bioprocesses, a usual objective is to stabilize the output of the bioreactors about a desired steady state (in wastewater industry, this value is typically chosen under the norm of authorized discharge). It happens more and more frequently that transient trajectories are expected also to maximize a product of interest.

We have begun to study the maximization of the gaseous production of methane in anaerobic processes over a given period of time on specific problems. For the moment we have proved that the optimal trajectory consists in approaching a unique singular arc as fast as possible when only one limiting substrate has to be converted,  but the problem is still open when involving several substrates. These works are part of the PhD work of A. Ghouali and W. Bouhafs.

Reference points in batch processes can be mimicked by a series of continuously stirred bioreactors in series at steady state (see applications \ref{wine} and \ref{module.contracts.CAFE}). We study the minimal time problem to drive the nutrients concentrations of a cascade of chemostats. The control variable is the dilution rates of each tank, under the constraint that each dilution rate is bounded by the one of the previous tank, that makes the system not locally controllable. For the particular case of two tanks with total mass at steady state, the planar feedback synthesis has been found but the problem is still under investigation for the general case.


%......................................................................
\subsubsection{Minimal time bioremediation of natural resources}
\label{section-bioremediation}
\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}, 
	\pers{Antoine}{Rousseau}
\end{participants}

In biological wastewater treatment (batch or continuous bioprocesses), one has always to separate biomass from the purified liquid phase at the output of the tanks, that is not possible when tanks are rather natural reservoirs such as lakes or water tables.

We have proposed a new operation strategy that consists in treating with the help of a bioreactor aside. No bacteria are introduced in the reservoir but water is pumped and treated by microorganisms in a smaller tank, and treated water returns to the reservoir after being separated from the biomass. Consequently, there is no need of a separation operation for the reservoir.

The minimal time control problem consists in controlling the flow rate for having the substrate concentration of the whole reservoir below a given reference value as fast as possible.


Last year, we have determined analytical expressions of optimal feedback strategies for a general class of growth functions under the assumptions that the volume of the bioreactor is much smaller than the reservoir one, and that the spatial repartition of the concentration of the pollutant in the reservoir can be modelled by simple spatial representations: either perfectly mixed or discrete one directional gradient \cite{GAJARDO:2011:HAL-00521118:2,GRRH11-automatica}. This year, we have studied more realistic spatial motifs:
\begin{itemize}
\item[-] dead-zones: we have shown that the optimal synthesis is identical to the perfectly mixed case, even though the time to reach the target is larger \cite{GRRR11},
\item[-] two parallel zones, allowing to control the repartition of the flow rate between the two zones. Without diffusion between the zones, the optimal solution is almost straightforward and under investigation in presence of lateral diffusion.
\end{itemize}

This work is mainly achieved in cooperation with Chilean researchers and PhD students within the associated team DYMECOS.




\end{module}
%---------------------------------------------------------------------





%---------------------------------------------------------------------

\begin{module}{resultats}{applications}{Applications}

%......................................................................
\subsubsection{Modelling and control of Anaerobic Digestion processes}
\label{SectionMBR}

\begin{participants}
	\pers{Amine}{Charfi},
	\pers{Radhouene}{Fekih-Salem},
	\pers{J\'er\^ome}{Harmand},
	\pers{Boumediene}{Benyahia},
	\pers{Tewfik}{Sari}
\end{participants}


We consider the AM2 or AMOCO model developed in \footcite{Bernard2001} and extend both the model in itself
and its analysis to the following cases:
\begin{itemize}
\item Depending on the AM2 model parameters, the steady states were
analytically characterized and their stability were analyzed.
Following this study, it was shown that the overloading
tolerance, a parameter proposed in \footcite{Hess2007}
to on-line monitoring anaerobic processes, may be not adapted under
certain operating conditions and even lead to bad operating decisions.
\item Within the framework of the PhD theses of Amine Charfi and Boumediene Benyahia,
we have included the fouling dynamics of membranes into
the AM2 and we have analyzed
the resulting model (called the AM2b).
\item We actually work towards two directions: (i) we are extending these results
in including into the AM2 an additional process, {\it i. e.} the hydrolysis step in order 
to study bioprocesses treating solid waste (the resulting model being called the AM3); (ii) we try to find
links between complex models such as the ADM1 model
and simple models such as the AM2b or the AM3.
\end{itemize}



%%......................................................................
%\subsubsection{Cellobiose hydrolysis}
%
%\begin{participants}
%	\pers{J\'er\^ome}{Harmand}, 
%	\pers{Alain}{Rapaport}, 
%	\pers{Tewfik}{Sari}, 
%	\pers{Ahlem}{Saddoud}
%\end{participants}
%
%In the paper zzz, we study a mathematical model for an enzymatic
%reaction with both
%substrate and product inhibition in two interconnected Continuous
%Stirred Tank Reactors
%(ICSTRs) where one of them is a dead zone. A sufficient condition which
%guarantees the steady
%states multiplicity was achieved. The main purpose of this work is to
%estimate the kinetic
%and operating parameters which give, in experimental data, the steady
%states multiplicity and
%guarantees the presence of the best equilibrium that has the optimum
%biological performance
%in a non homogeneous reaction system.


%......................................................................
\subsubsection{Modelling and control of cascade biosystems to mimic batch wine making processes}
\label{wine}

\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}, 
	\pers{Jos\'e}{Fernandez}
\end{participants}


An experimental setup of four tanks connected in series has been designed by the research unit SPO (Montpellier) for studying four physiological stages of yeast as steady state.
The manipulated variables are the flow rates $Q_{i}$ of each tank with the constraint $Q_{i}\geq Q_{i-1}\geq 0$, and the objective is to reach simultaneously four set-points in the four tanks. We are studying two kinds of control strategies:
\begin{itemize}
\item a linearizing feedback law that drives exponentially the dynamics to the target. This is not the fastest strategy but is has good robustness properties. Nevertheless, the inputs constraint imposes to use saturation functions that provide satisfactory convergence in simulations but that is hard to prove mathematically.
\item a minimal time feedback. Due to lack of local controllability imposed by the constraint on the inputs, the optimal synthesis is not smooth with the presence of ``barriers'' (see Section \ref{optimal-control}).
\end{itemize}
Those feedback laws will be implemented and tested on real pilot plant at SPO lab in the scope of the European project CAFE described in Section \ref{module.contracts.CAFE}.

%......................................................................
\subsubsection{Modelling and simulating terrestrial plant ecological dynamics}
\label{sec.recent.ecological.dynamics}

\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Ihab}{Haidar}
\end{participants}

This study is part of the ANR Syscomm MODECOL that is done in collaboration particularly with the University of Rennes I, the University of La Rochelle and INRIA. This is the second year of the three years program. We propose a stochastic individual-based model for clonal plant dynamics in continuous time and space, focusing on the effects of the network structure of the plants on the reproductive strategy of ramets. This model is coupled with an explicit advection-diffusion dynamics for resources. We develop a partially exact simulation scheme of the model ; the capacity of the model to reproduce specific features of clonal plants, such as their efficiency to forage resources over the field, is numerically studied. Next, we propose a large population approximation of the model for phalanx-type populations, taking the form of an advection-diffusion PDE for population densities, where the influence of the local graph structure of the plant takes the form of a nonlinear dependence in the gradient of resources. This year we improved the simulation code that was proposed last year and made three communications in international conferences \cite{campillo2011f} \cite{CAMPILLO:2011:HAL-00651732:1} \cite{CAMPILLO:2011:HAL-00651758:1}.




%......................................................................
\subsubsection{Modelling and inferring agricultural dynamics}
\label{sec.recent.agricultural.dynamics}
\begin{participants}
	\pers{Fabien}{Campillo}, 
	\pers{Angelo}{Raherinirina}
\end{participants}


The International Laboratory LIRMA supports this work that is done in collaboration with the University of Fianarantsoa in Madagascar and with Dominique Herv\'e (IRD, Fianarantsoa, Madagascar). The aim is to study the dynamics of agricultural plots on the edge of primary forest. In \cite{campillo2011-inria}
we propose a Markov chain model where the transition matrix is estimated both by maximum likelihood and Bayesian approaches. We also test if the Markov chain model is adapted to this problem. In an ongoing work we develop semi-Markov models for an extended data set.



%......................................................................
\subsubsection{Modelling and simulating microbial ecosystems in soils}
\label{sec.recent.soil.ecosystems}
\begin{participants}
	\pers{Ihab}{Haidar}, 
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}
\end{participants}


The team studies simple representations of the spatial inhomogeneity for bioprocesses, in terms of networks of interconnected compartments. Each compartment is modelled as a perfectly mixed bioreactor. Simulation of such networks is performed with the software developed by the VITELBIO project (see \ref{module.logiciels.VITELBIO}). Comparisons of simple structures (cascade of bioreactors) with numerical simulators based on REV (Representative Elementary Volumes) have been performed. The objective is to understand the role of the topology of the network on the biological functions of the overall system. For the moment simple configurations with two to four nodes and one single species have been investigated.

In addition, we study in simulation how software implementations of transport reactions models such as MIN3P, can mimic such simple configurations. Transport reactions models are often used in the community of soil functioning modelling by simulation. Here we compare their numerical computation with the exact solutions that can be approximated with a a good accuracy using  classical o.d.e. solvers. We found that one has to be careful when the trajectories are close to a non-hyperbolic equilibrium \cite{HRG11b,HRG11bb}.


%......................................................................
\subsubsection{Numerical optimisation in non perfectly mixed tanks}

\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}, 
	\pers{Antoine}{Rousseau}
\end{participants}


The optimisation of series of bioreactors in terms of minimizing the total residence time have been already investigated in the literature.
In such models, the space has a one-dimensional representation. There is comparatively much less work on 2D or 3D space models.
We consider different shapes of reactors of the same volume, and simulate with the help of multi-physics numerical software the coupling of the hydrodynamics laws in 3D or 2D (under cylindrical symmetry), solved numerically by the Navier-Stokes equations, with the system
of differential equations of biotic/abiotic concentrations \cite{BHIRR11}.

We are studying the influence of the shape of the domain and possible obstacles on the output concentration at steady state.

With A. Rousseau (EPI MOISE, INRIA Rh\^one-Alpes) we study with simulation of 2D Navier-Stokes equations the benefits of having several pumping points and how to control the pumps speed
to minimize the treatment duration for the bioremediation of natural reservoirs presented in Section \ref{section-bioremediation}. We
evaluate the feedback strategies that are derived to be optimal for simple models of o.d.e. in the more realistic framework of hydrodynamics simulation.


%......................................................................
\subsubsection{Individual-based models for the bacterial degradation of the cellulose}

\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Chlo\' e}{Deygout},
	\pers{Marc}{Joannides}
\end{participants}

We propose an individual-based model for the degradation of one cellulose bead (dozens of micrometers in diameter) by cellulolytic bacteria. Our aim is to determine the macroscopic degradation behavior. The initial stages of the degradation process may involve a very limited number of bacteria that cannot be properly modelled by classical models based on deterministic equations. In the present work we only consider a two-dimensional model for the degradation of a cellulose disc.

%......................................................................
\subsubsection{Non-linear filtering for the chemostat}

\begin{participants}
	\pers{Boumediene}{Benyahia},
	\pers{Fabien}{Campillo},
	\pers{J\'er\^ome}{Harmand},
\end{participants}


We propose numerical non-linear filtering approaches for the identification of non-observed components of dynamical systems in the context of the chemostat. This recently started study relies on the work of the project-team in the stochastic modelling of the chemostat. In a preliminary work we consider the bootstrap particle filter.

\end{module}
%----------------------------------------------------------------------



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Section contrats
%%% Il y a des modules ici
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%----------------------------------------------------------------------
\begin{module}{contrats}{bioinh}{BioInh}
\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}, 
	\pers{Tewfik}{Sari}
\end{participants}

The project BioInh (Modelling and optimization of bio-conversion of plant materials in inhomogeneous media)
proposed by the UMR IATE (Ing\'enierie des Agropolym\`eres et Technologies Emergentes, Montpellier) and the
team MODEMIC is funded by the Agropolis foundation (Montpellier) for two years since 2009. The project aims
at studying inhomogeneity effects in enzymatic reactors with the help of models of cascade of reactors. The
presence of inhibition in the growth function may lead to unstabilities and drive the system toward several
possible equilibria, one of them only being interesting in terms of yield and productivity. First experiments have
been run but have not been finished due the interruption of our former postdoc for health reasons.


\end{module}
%----------------------------------------------------------------------


%----------------------------------------------------------------------
\begin{module}{contrats}{cafe}{CAFE}
\label{module.contracts.CAFE}
\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Jos\'e}{Fernandez},
 	\pers{Alain}{Rapaport}
\end{participants}

The objective of the CAFE European project is to provide new paradigms
for the smart control of food processes, on the basis of four typical processes in the areas of bioconversion,
separation, preservation and structuring. The novelty of the project lies in the capacity of combining PAT
(Process Analytical Technology) and sensing devices with models and simulation environment with the
following objectives:

1- to extract as much as possible information from the process/plant in the form of precise estimations
of unmeasured variables defining, in particular, product quality, and of physical parameters changing
as the process dynamics does or difficult to know beforehand;

2- to save and encode the information in a reliable and usable way, basically via physical/deterministic
models;

3- to develop control methods to keep uniform quality and production, despite the variability in the raw
material and/or to respond to sudden changes in the demand.

MODEMIC is involved in the wine making optimization part of the project which constitutes the PhD thesis of Jos\'e Fernandez (see Sections \ref{optimal-control} and \ref{wine}).




\end{module}
%----------------------------------------------------------------------

%----------------------------------------------------------------------
\begin{module}{contrats}{dimimos}{DIMIMOS}
\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}
\end{participants}


This fundamental research project aims at better understanding the functional microbial soil ecosystems with
respect to the turnover of soil organic matter (SOM). More specifically, we aim at evaluating the role of the
microbial diversity in transforming SOM, in order to better manage the carbon in its biochemical global cycle
within agro-ecosystems. This project must deliver new insights for managing agricultural productivity (allow
better agricultural practices) while maintaining a high quality of soil over the long term.

The results obtained within the E-MICRAM project turn out to be of direct interest for the DIMIMOS project (see Section \ref{module.contrats.rnsc}).

\end{module}
%----------------------------------------------------------------------


%----------------------------------------------------------------------
\begin{module}{contrats}{disco}{DISCO}
\label{module.contrats.DISCO}
\begin{participants}
	\pers{Fabien}{Campillo}, 
	\pers{Chlo\' e}{Deygout},
	\pers{Bart}{Haegeman}, 
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Claude}{Lobry}, 
	\pers{Alain}{Rapaport},
	\pers{Tewfik}{Sari}
\end{participants}


DISCO (Multi-scale modelling bioDIversity Structure COupling in biofilms) is a three years project funded by the ANR SYSCOMM since the end of 2009, that aims at developing and studying computational and mathematical models of biofilm dynamics, taking into account the biodiversity (distribution of bacteria species) and spatial structure.

In 2010, C. Deygout has been hired as a postdoctoral fellow. She is developing a double modelling approach of the formation of single species biofilms in tubular bioreactors. One approach is based on a IBM model with a large number of individuals, while the second one is a system of PDE at the macroscopic level (see Section \ref{sec.ibm.disco}). We aim at understanding the links between the characteristics of the random events (consumption, growth, death, movement) at individual level with the functional expressions of growth, detachment and attachment at the population level.

In 2011, C. Deygout has spent most of her time at HBAN Cemagref to contribute to experiments in tubular plug-flow reactors, the biofilm being observed with the help of confocal microscopy. At that time, the set-up has not allowed yet a fair comparison between model simulations and experimental data.

A new collaboration has been launched with the HBAN team at Cemagref Antony, within this project, about the modelling of cellulose degradation. Cellulose is typically available in small balls (but ten times larger than the average size of microorganisms) that are first converted by enzymatic activity into carbon substrate that can then be assimilated by the microorganisms. Some of the microorganisms are attached to these balls, creating a particular aggregates structure.

\end{module}
%----------------------------------------------------------------------


%----------------------------------------------------------------------
\begin{module}{contrats}{modecol}{MODECOL}
\label{module.contrats.modecol}
\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Ihab}{Haidar}
\end{participants}



The ANR SYSCOMM Project MODECOL (2009-2011) involves three INRIA project-teams (MODEMIC, MAESTRO and TOSCA) with the UMR Ecobio (Rennes, France), the University of La Rochelle and the Universities of Houston and Berkeley. The aim of the MODEMIC component  is to propose individual-based models for terrestrial prairial plant
communities' dynamics in the context of water purifying from nitrate and pesticides. This year the INRIA team proposed a new model
\cite{campillo2011f},
its analysis and the development of a simulation software. The project-team was also involved in the organization of a session at the 54th Symposium of the International Association for Vegetation Science (IAVS)
in Lyon (20-24 June 2011). This work has been presented in three international conferences \cite{CAMPILLO:2011:HAL-00651732:1} \cite{CAMPILLO:2011:HAL-00651758:1} \cite{campillo2011f}.

\end{module}
%----------------------------------------------------------------------


%----------------------------------------------------------------------
\begin{module}{contrats}{rnsc}{E-MICRAM}
\label{module.contrats.rnsc}
\begin{participants}
	\pers{Florent}{Dayle}, 
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Alain}{Rapaport}
\end{participants}



E-MICRAM is a project supported within the framework of the French RNSC (R\'eseau National des Syst\`emes Complexes).
It aims at investigating whether the performance of a microbial ecosystem is related to its diversity or not.

With researchers of UMR Eco \& Sols (Montpellier) and Bioemco (Paris-Grignon), we are investigating the influence of the size of samples on the probability of obtaining a ``positive'' reconstituted ecosystem, on the basis of experimental data (MSc subject of F. Dayle).


\end{module}
%----------------------------------------------------------------------

%----------------------------------------------------------------------
\begin{module}{contrats}{vitelbio}{VITELBIO}
\label{module.contrats.VITELBIO}
\begin{participants}
	\pers{Fadia}{Gafsi}, 
	\pers{Ihab}{Haidar}, 
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Mouna}{Jraou}, 
	\pers{Alain}{Rapaport}
\end{participants}


The former ARC VITELBIO (VIrtual TELluric BIOreactors) gathers biologists, agronomist, micro-biologists, soil physicists and mathematicians about the modelling of heterogeneity in soil, and its role on microbiological functions. An important activity of the project has consisted in the development of a user friendly software for designing networks, simulating the dynamics and exploiting the results, with the help of ITK Company (see Section \ref{module.logiciels.VITELBIO}).

The software has allowed to explore the yield properties of several configurations of interconnections (serial, parallel, diffusive, dead zone and more sophisticated ones). Part of them has then being analytically studied in the PhD work of  H. Haidar.

The VITELBIO project has been also financed by INRA for the purchase of four experimental chemostats
settled at the LBE Narbonne. The training period of F. Gafsi and M. Jraou at the LBE and then in Montpellier have aimed to compare numerical
predictions with real experiments.

\end{module}
%----------------------------------------------------------------------


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Section International
%%%
%%% Dans cette section, certains modules sont **normalises**.
%%% Par consequent, la liste de ces modules
%%% est (penser \`a mettre le nom de section au moins une fois):
%%%
%%% \begin{module}{international}{regional}{Regional Initiatives}
%%% \begin{module}{}{national}{National Initiatives}
%%% \begin{module}{}{europe}{European Initiatives}
%%% \begin{module}{}{international}{International Initiatives}
%%% \begin{module}{}{visites}{Exterior research visitors}
%%%
%%% On peut, en outre, rajouter des modules pour inserer ce qui ne
%%% rentre pas dans les categories ci-dessus. Pour chacun des modules
%%% listes, les actions proprement dites apparaitront en
%%% \subsubsection, et les listes de {participants} seront ventilees en
%%% consequence, par \subsubsection (et non plus par module)
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%---------------------------------------------------------------------
\begin{module}{international}{international}{International Initiatives}

%.....................................................................
\subsubsection{Associated team DYMECOS}

\begin{participants}
	\pers{Fabien}{Campillo}, 
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Claude}{Lobry}, 
	\pers{Alain}{Rapaport}, 
	\pers{Tewfik}{Sari}, 
	\pers{Terence}{Bayen}
\end{participants}

DYMECOS is an associated team with Chile, mainly with CMM (Centro de Modelamiento Matem\'atico), Univ. de Chile, Santiago, DIM
(Departamento de Ingenier{\'\i}a Matem\'atica), Universidad de Chile, Santiago and Departamento de Matematica, Universidad Tecnica Federico Santa Maria (UTFSM).

Two kinds of investigations have been conducted:
\begin{itemize}
\item minimal time control problems of fedbatch processes with several species, and optimal strategies for the bioremediation of natural water resources,
\item stochastic modelling of the chemostat.
\end{itemize}

In 2011, the team has been visited by the Chilean researchers D. Jeson (U. Frontera), P. Gajardo (UTFSM, Valparaiso), H. Ramirez (DIM, Santiago) and G. Ruiz (PUCV, Valparaiso). Two Chilean students V. Riquelme and E. Tapia have spent some time in MODEMIC team, and two PhD students J. Fernandez and M. Sebbah have been sent to Chile.

A second Franco-Chilean Workshop on Bioprocess Modelling, co-organized with the Chilean partners, is scheduled in January at 
Puc\'on. The workshop gathers mathematicians, process engineers and micro-biologists.

Activities and results are described on the web site \href{https://sites.google.com/site/eadymecos}{Eadymecos}.

%.....................................................................
\subsection{MOMARE}
\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{J\'er\^ome}{Harmand},
	\pers{Marc}{Joannides},
	\pers{Claude}{Lobry},
	\pers{Alain}{Rapaport}
	\pers{Tewfik}{Sari}
\end{participants}

The MODEMIC Project-Team is coordinator of the Stic-Amsud project MOMARE ``Mathematical models of natural resources management'' between INRIA, IMCA (Peru), DIM-CMM (Chile) and PLAMEDA (Argentina). This project has funded the travel of researchers from France to South America and from South America to France. In this context, the MODEMIC Project-Team was visited by E. Ocana (IMCA), H. Ramirez-Cabrera (DIM-CMM), J. Fontbona (DIM-CMM). The project is structured in two subthemes: ``microbiology'' (bioreactors, waste water treatment) and ``harvesting'' (``fishery'' and ``forestry'', but also ``mines''). The project will be ended
at the second French-Chilean Workshop on Bioprocess Modelling
January 16-17, 2012 (Puc\'on, Chile).

%.....................................................................
\subsection{TREASURE}
\begin{participants}
	\pers{J\'er\^ome}{Harmand}, 
	\pers{Claude}{Lobry}, 
	\pers{Tewfik}{Sari}
\end{participants}

The TREASURE network benefits from financial support from INRIA, INRA
and African partners of about 20 Keuros/year for three years
(2009-2011). In addition, a European IRSES (called COADVISE) project
ending in 2012 includes 42 man months available for exchanging
PhD and postdoc students within 48 months.

This project was recently extended in a renewed form for a period of four years from January 2012.

%.....................................................................
\subsection{LIRIMA Stic-Mada}
\begin{participants}
	\pers{Fabien}{Campillo},
	\pers{Angelo}{Raherinirina}
\end{participants}

The MODEMIC Project-Team is coordinator of the LIRIMA/Stic-Mada project for the theme: modelling and management of natural resources. The thesis of Angelo Raherinirina is prepared in this context in association with the University of Fianarantsoa and IRD. In Antananarivo (Madagascar),
September 19-30, we organized the first ``Ecole Math\'ematique Africaine'' together with the CIMPA. One of the themes of this school was on Markov modelling for ecological successions.


\end{module}
%----------------------------------------------------------------------



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Information sur les donn\'ees import\'ees : projets europ\'eens
%%%
%%% Les donn\'ees sur vos partenariats europ\'eens sont issus de la
%%% Direction des Partenariats Europ\'eens (DPE) via l'entrep\^ot de donn\'ees.
%%% Les donn\'ees affich\'ees sont : Nom du projet, Type Defi,
%%%                              Instrument, Duration, Coordinator, Others partners.
%%%
%%% Compl\'etez ce qui manque : r\'esum\'e, Others partners (on attend des noms de pays), ....
%%%
%%% En cas de probl\`eme : raweb-support@inria.fr
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%\begin{module}{}{europe}{European Initiatives}
%
%% projet FP7
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Donnees  construites d'apres les informations de la base DPE pour 2011
%% Source : https://drone.inria.fr/
%% Date : mardi 15 novembre 2011, 10:04:17 (UTC+0100)
%%
%
%
%
%
%% Eureka, COST...
%\subsection{Collaborations in European Programs, except FP7}
%
%% respecter le format
%\begin{itemize}
%\XMLaddatt*{type}{sanspuces}
%	\item Program:
%	\item Project acronym:
%	\item Project title:
%	\item Duration: mois ann\'ee d\'ebut - mois ann\'ee fin
%	\item Coordinator:
%	\item Other partners: organisme, labo (pays)
%	\item Abstract:
%\end{itemize}
%
%...
%
%% organisation europeenne
%\subsection{Major European Organizations with which you have followed Collaborations}
%
%% respecter le format
% \begin{itemize}
% \XMLaddatt*{type}{sanspuces}
%        \item Partner 1: organisme 1, labo 1 (pays 1)
%        \item Sujet 1 (max. 2 lignes)
% \end{itemize}
% \begin{itemize}
% \XMLaddatt*{type}{sanspuces}
%       \item Partner 2: organisme 2, labo 2 (pays 2)
%       \item Sujet 2 (max. 2 lignes)
% \end{itemize}
%
%...
%
%
%\end{module}
%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Information sur les donn\'ees import\'ees : \'equipes associ\'ees et autres actions internationales
%%%
%%% La rubrique est pr\'e-remplie avec les 4 items suivants :
%%% - Inria Associate Teams : les donn\'ees sur vos \'equipes associ\'ees sont issues
%%% de la base de la Direction des Relations Internationales (DRI) alimentant l'entrep\^ot de donn\'ees.
%%% Si vous voyez des erreurs, signalez-les \`a raweb-support@inria.fr.
%%%
%%% - Inria International Partners : indique un partenariat international important pour votre \'equipe,
%%% hors Equipes Associ\'ees et hors participation aux programmes internationaux mentionn\'es ci-dessous (cf. 4).
%%%
%%% - Visits of International Scientists : chercheurs invit\'es, professeurs invit\'es (via une universit\'e partenaire),
%%%  stagiaires internships. Les informations sur les Internships sont issues de la base de donn\'ees
%%%  de la DRI alimentant l'entrep\^ot de donn\'ees. Si vous voyez des erreurs, signalez-les \`a raweb-support@inria.fr.
%%%
%%% - Participation in other International Programs :
%%%    * implication dans les activit\'es des laboratoires conjoints \`a l'\'etranger (JLPC Etats-Unis ; LIRIMA Afrique ; JFLI Japon ; LIAMA Chine)
%%%    * participation aux diff\'erents programmes soutenus par la DRI et/ou des financeurs externes
%%%	  (Euromediterran\'ee 3+3, STIC Alg\'erie, STIC Tunisie, STIC AmSud, Math AmSud, Inria-CNPq, Inria-FAPs,
%%%  Inria-CONICyT, Inria-MINCyT, Reussi USA, Inria@SILICONVALLEY, STIC Asie, Inria-Russie, Autres)
%%%
%%% En cas de probl\`eme : raweb-support@inria.fr
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%\begin{module}{}{international}{International Initiatives}
%
%\subsection{INRIA Associate Teams}
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Donnees construites d'apres les informations de la base DRI pour 2011
%% Source : https://drone.inria.fr/
%% Date : mardi 15 novembre 2011, 10:04:17 (UTC+0100)
%%
%
%
%
%
%\subsection{INRIA International Partners}
%%partenariat international phare, sans contrat, sans financement
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Donnees construites d'apres les informations de la base DRI pour 2011
%% Source : https://drone.inria.fr/
%% Date : mardi 15 novembre 2011, 10:04:17 (UTC+0100)
%%
%
%
%
%
%\subsection{Visits of International Scientists}
%%chercheurs invites, profs invites (via universite), stagiaires internships
%
%\subsubsection{Internship}
%
%
%
%
%\subsection{Participation In International Programs}
%%participation aux differents programmes
%
%...
%
%\end{module}
%






%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Section diffusion des resultats
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%




%---------------------------------------------------------------------
\begin{module}{diffusion}{animation}{Animation of the scientific community}

\begin{itemize}

\item A. Rapaport is presently the head of the UMR INRA-SupAgro MISTEA (Mathematics, Informatics and STatistics for Environment and Agronomy) where the team is housed. A. Rapaport is:
member of the piloting board of the ``modelling'' axis of the LabEx Numev (Digital and Hardware Solutions, Modelling for the Environment and Life Sciences)) at Montpellier; member of the scientific board of the ``Ecotechnologies'' department of IRSTEA (Cemagref); member of the scientific board of the ``BIOS'' department of CIRAD.

\item F. Campillo is member of the NICE (long term invited scientists selection); deputy elected member of the INRIA Scientific Council; member of the internal communication working group of INRIA Sophia Antipolis. F. Campillo was member of the INRA selection board for the selection of junior scientists (statistics and modelling).

\item J. Harmand is the responsible for the treasure-2 network (a 3+3 Euromed project) that has been accepted for funding for the next 4 years); 
member of the scientific council of the Environment and agronomic
INRA department; member of the advisory board of the INRA metaprogram MEM (Meta-omic and Microbial Ecosystems); member
of the INRA evaluation commission STEA (``Sciences de la Terre, de l'eau et de l'atmosphère''). 

\item F. Campillo and A. Rapaport are co-organizers of the MIA-Montpellier network that gathers about 70 researchers on applied mathematics and informatics for environmental and agronomics sciences on Montpellier [\href{http://sites.google.com/site/miamontpellier/}{link}].

\item J. Harmand and A. Rapaport are responsible of the INRA network MODYM (MOd\`eles DYnamiques et M\'etabolites).

\item B. Haegeman is Academic Editor for PLoS ONE.

\end{itemize}

\end{module}
%---------------------------------------------------------------------

%---------------------------------------------------------------------
\begin{module}{diffusion}{seminars}{Seminars and schools}

The MODEMIC project-team animates several seminars:
\begin{itemize}

\item The MODEMIC seminar on mathematical modelling [\href{http://www-sop.inria.fr/modemic/seminaire/}{link}].

\item  I3M and MODEMIC working group on stochastic models for ecology and biology [\href{http://www-sop.inria.fr/modemic/personnel/campillo/GT-modelisation.html}{link}]; this working group is supported by the  ``laboratory of excellence'' (LabEx) \href{http://www2.lirmm.fr/numev/}{NUMEV} (Digital and Hardware Solutions, Modelling for the Environment and Life Sciences).

\item
Within the framework of the TREASURE network, an international school has been co-organized with the INRA LBE in Narbonne From June the 6th
to the 10th. About 35 Master, PhD students, junior as well as senior researchers from 6 countries have followed these courses
about water treatment technology.

\item MODEMIC  has organized its internal annual seminar in Saint-Martin-de-Londres, France, November September 6-8, 2011.

\end{itemize}

\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{diffusion}{enseignement}{Teaching}

\begin{itemize}

\item F. Campillo gave a 10 hours lecture on ``Markov models for successions in ecology and agronomy''
at the first ``Ecole Math\'ematique Africaine'' organized by the CIMPA and the LIRIMA in Antananarivo (Madagascar), September 19-30.


\item F. Campillo and M. Joannides have given a 20 hours lecture on ``Stochastic modelling of ecosystems'' at the Master 2 in Biostatistics in Universit\'e de Montpellier II.

\item Fabien Campillo, C. Lobry and T. Sari gave a 6h lecture at the Summer School/Workshop  ``Mod\`eles math\'ematiques de la dynamique des populations'',  April 18--22, Ecole Polytechnique, Tunis.

\item A. Rapaport gave a 25 hours lecture on differential equations with applications in the ``Practical Mathematics'' module for 1st year students in MSc in Mathematics at University Montpellier II.

\item F. Campillo, J. Harmand and A. Rapaport are in charge of a new 50 hours module on ``Introduction to mathematical modelling'' in the new MSc program ``STIC - Environnement'' at University Montpellier II.

\item F. Campillo and A. Rapaport have delivered a 20 hours doctoral module at University Montpellier II, entitled ``Modelling for biology and ecology -- mathematical and computational methods''.

\item F. Campillo, J. Harmand, A. Rapaport and T. Sari have given introductive lectures on mathematical modelling for 1st year students of SupAgro Montpellier.

\item A. Rapaport has given three lectures on Modelling and numerical simulations at the ``EcoSyst\`emes'' Master at University of Montpellier II.

\item J. Harmand, C. Lobry, T. Sari delivered introductory lectures on mathematical modelling of bioprocesses at the Tlemcen school organized by TREASURE.

\end{itemize}
 
\end{module}
%---------------------------------------------------------------------




%---------------------------------------------------------------------
\begin{module}{diffusion}{encadrement}{PhD's}

Defended thesis:
\begin{itemize}
 
\item Ihab Haidar, ``Dynamiques microbiennes et mod\'elisation des cycles biog\'eochimiques terrestres'';
grant: MESR (Univ. Montpellier II);
started october 2008, defended december 2011;
advisors: A. Rapaport and F. G\'erard (UMR Eco \&\ Sols).

		  
\end{itemize}

Theses in progress:
\begin{itemize}

%\item HdR : nom du chercheur, titre du m\'emoire, nom de l'Universit\'e, date de soutenance
 
 
\item Mamadou Lamine Diagne, ``Mod\'elisation math\'ematique du Typha''; 
grant: AUF;
thesis in co-supervision Mulhouse-Saint Louis (Senegal);
started in October 2009;
advisors: T. Sari and M.T Niane (Saint Louis, Senegal).

\item
Boumediène Benyahia, ``Mod\'elisation et contr\^ole de bior\'eacteurs \`a membrane'';
grant: Coadvise and Treasure;
thesis in co-supervision Montpellier-Tlemcen (Algeria);
started in October 2008;
advisors: J. Harmand and B. Cherki (Tlemcen, Algeria).

\item
Jos\'e Fernandez, ``Modelling and control of cascade reactors for the study of wine making'';
grant: INRIA/CONYCIT(Chile) and INRA grant;
started april 2009;
advisors: A. Rapaport.

\item
Radhouene Fekih-Salem, ``La compétition et la coexistence dans le Chemostat'';
grant: Averroes; 
thesis in co-supervision Montpellier-Tunis;
started in October 2010;
advisors: A. Rapaport, T. Sari and N. Gmati (Tunis).

\item
Jos\'e Fernandez, ``Modelling and control of cascade reactors for the study of wine making'';
grant: INRIA/CONYCIT(Chile) and INRA grant;
started april 2009;
advisors: A. Rapaport and J. Harmand.


\item
Amel Ghouali, ``control en tamps minimal des réacteurs de digestion anaérobie'';
grant: Averroes; 
thesis in co-supervision Montpellier-Tlemcen (Algeria);
started in October 2011;
advisors: J. Harmand and B. Cherki  (Tlemcen, Algeria).

\item Angelo Raherinirina, ``Mod\'elisation markovienne de dynamique d'usage des sols''; 
grant: AUF, SCAC Madagascar, LIRIMA; 
started March 1st 2009; 
advisors: F. Campillo and R. Rakotozafy (Uni. Fianarantsoa Madagascar).

\item Coralie Fritsch, ``Simulation et analyse de mod\`eles individu-centr\'es d'\'ecosyst\`emes bact\'eriens pour des proc\'ed\'es biotechnologiques'', \'ecole doctorale I2S; 
grant: INRA Metaprogram MEM and Univ. Montpellier II; 
started October 1st 2011; 
advisors: F. Campillo, M. Joannides, J. Harmand.


\item Guilherme Pimentel, ``Modelling and control of bioreactors with membrane''; 
grant: Univ. Mons (Belgium) and INRA; 
thesis in co-supervision Montpellier-Mons;
started October 2011; 
advisors: A. Rapaport, J. Harmand and A. VandeWouver (Univ. Mons).

\item Amine, ``Mod\'elisation du colmatage dans les r\'eacteurs  à membranes''
grant: Coadvise and University of Tunis; 
started October 2009; 
advisors: J. Harmand and Nihel Benamar.


\end{itemize}

\end{module}
%---------------------------------------------------------------------


%---------------------------------------------------------------------
\begin{module}{diffusion}{committees}{Participation to thesis committees}

\begin{itemize}

\item
A. Rapaport (member): J.D. Mathias, ``De la double mod\'elisation \`a la validation exp\'erimentale par des m\'ethodes de mesure de champs sans contact du comportement des mat\'eriaux'', th\`ese d'Habilitation \`a Diriger des Recherches, Univ. B. Pascal, Clermont II.

\item
A. Rapaport (member): M. Mazade, ``Ensembles localement prox-r\'eguliers et in\'equations variationnelles'', Univ. Montpellier II.

\item
A. Rapaport (advisor): I. Haidar, ``Dynamiques microbiennes et mod\'elisation des cycles biog\'eochimiques terrestres'', Univ. Montpellier II.

\item F. Campillo (referee): M. Xuan-Binh Lam, ``Uncertainty quantication for stochastic subspace identification method'', Uni. Rennes I.

\item F. Campillo (referee): Mme Nelly Jean-Baptiste, ``Assimilation de donn\'ees pour l'estimation de l'\'etat hydraulique d'un am\'enagement hydro\'electrique du Rh\^one \'equip\'e de la commande pr\'edictive'', Uni. Toulouse III.

\end{itemize}

\end{module}
%---------------------------------------------------------------------


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Section bibliographie
%%%
%%% Il y a trois fichiers de bibliographie
%%%
%%% - modemic_refer2011.bib est inclus dans sa totalit\'e, contient les 10
%%%   r\'ef\'erences majeures du projet, cit\'ees par \refercite
%%%
%%% - modemic2011.bib pour la  bibiographie normale, ne sont extraites que
%%%   les r\'ef\'erences invoqu\'ees par \cite ou bien par \nocite
%%%
%%%   A la demande de la direction, nous vous demandons d'ajouter un champ "x-pays"
%%%   \`a chaque bibtex pour lesquels un des co-publiants est "\'etranger".
%%%   L'objectif est de faire appara\^{\i}tre, \`a travers les copublications, les travaux
%%%   r\'ealis\'es en coop\'eration avec des pays \'etrangers.
%%%
%%% - modemic_foot2011.bib contient les citations utilis\'ees en note
%%%   de bas de page, cit\'ees par \footcite
%%%
%%% Pour les articles \`a para\^{\i}tre utilisez : NOTE = {to appear}
%%% Mettez les URL dans le champ URL, et non dans les champ ps, pdf ou note...
%%% V\'erifier si les noms des auteurs sonts corrects
%%%
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Information sur les donn\'ees export\'ees : biblio
%%%
%%% Votre biblio de l'ann\'ee est issue de Hal http://hal.inria.fr/, vous pouvez y d\'eposer
%%% vos publications manquantes et reg\'en\'erer votre trame. Attention, pour
%%% cause de validation, il peut y avoir un d\'elai de 24h avant la mise en
%%% ligne et l'int\'egration \`a la trame.
%%%
%%% Url consulation/d\'ep\^ot des documents : http://hal.inria.fr/
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%% MODEMIC
%% Date : mardi 15 novembre 2011, 10:04:17 (UTC+0100)
%% Source rapport latex MODEMIC : http://ralyx.inria.fr/Raweb/modemic/uid0.html
%% Source donnees bastri SR0442X : https://bastri.inria.fr/FichesEquipes/structureinria/siid/SR0442X/look
%% Source donnees bil MODEMIC : https://bil.inria.fr/
